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Fujii T, Iizawa Y, Kobayashi T, Hayasaki A, Ito T, Murata Y, Tanemura A, Ichikawa Y, Kuriyama N, Kishiwada M, Sakuma H, Mizuno S. Radiomics-based prediction of nonalcoholic fatty liver disease following pancreatoduodenectomy. Surg Today 2024; 54:953-963. [PMID: 38581555 DOI: 10.1007/s00595-024-02822-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 01/09/2024] [Indexed: 04/08/2024]
Abstract
PURPOSE Predicting nonalcoholic fatty liver disease (NAFLD) following pancreaticoduodenectomy (PD) is challenging, which delays therapeutic intervention and makes its prevention difficult. We conducted this study to assess the potential application of preoperative computed tomography (CT) radiomics for predicting NAFLD. METHODS The subjects of this retrospective study were 186 patients with PD from a single institution. We extracted the predictors of NAFLD after PD statistically from conventional clinical and radiomic features of the estimated remnant pancreas and whole liver region on preoperative nonenhanced CT images. Based on these predictors, we developed a machine-learning predictive model, which integrated clinical and radiomic features. A comparative model used only clinical features as predictors. RESULTS The incidence of NAFLD after PD was 43.5%. The variables of the clinicoradiomic model included one shape feature of the pancreas, two texture features of the liver, and sex; the variables of the clinical model were age, sex, and chemoradiotherapy. The accuracy%, precision%, recall%, F1 score, and area under the curve of the two models were 75.0, 72.7, 66.7, 69.6, and 0.80; and 69.6, 68.4, 54.2, 60.5, and 0.69, respectively. CONCLUSIONS Preoperative CT-derived radiomic features from the pancreatic and liver regions are promising for the prediction of NAFLD post-PD. Using these features enhances the predictive model, enabling earlier intervention for high-risk patients.
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Affiliation(s)
- Takehiro Fujii
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
| | - Yusuke Iizawa
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Takumi Kobayashi
- School of Medicine, Faculty of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Aoi Hayasaki
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Takahiro Ito
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Yasuhiro Murata
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Akihiro Tanemura
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Yasutaka Ichikawa
- Department of Radiology, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Naohisa Kuriyama
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Masashi Kishiwada
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Hajime Sakuma
- Department of Radiology, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Shugo Mizuno
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
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Mostafavi L, Homayounieh F, Lades F, Primak A, Muse V, Harris GJ, Kalra MK, Digumarthy SR. Correlation of Radiomics with Treatment Response in Liver Metastases. Acad Radiol 2024; 31:3133-3141. [PMID: 38087718 DOI: 10.1016/j.acra.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/24/2023] [Accepted: 11/02/2023] [Indexed: 08/31/2024]
Abstract
RATIONALE AND OBJECTIVES To assess differences in radiomics derived from semi-automatic segmentation of liver metastases for stable disease (SD), partial response (PR), and progressive disease (PD) based on RECIST1.1 and to assess if radiomics alone at baseline can predict response. MATERIALS AND METHODS Our IRB-approved study included 203 women (mean age 54 ± 11 years) with metastatic liver disease from breast cancer. All patients underwent contrast abdomen-pelvis CT in the portal venous phase at two points: baseline (pre-treatment) and follow-up (between 3 and 12 months following treatment). Patients were subcategorized into three subgroups based on RECIST 1.1 criteria (Response Evaluation Criteria in Solid Tumors version 1.1): 66 with SD, 69 with PR, and 68 with PD on follow-up CT. The deidentified baseline and follow-up CT images were exported to the radiomics prototype. The prototype enabled semi-automatic segmentation of the target liver lesions for the extraction of first and high order radiomics. Statistical analyses with logistic regression and random forest classifiers were performed to differentiate SD from PD and PR. RESULTS There was no significant difference between the radiomics on the baseline and follow-up CT images of patients with SD (area under the curve (AUC): 0.3). Random forest classifier differentiated patients with PR with an AUC of 0.845. The most relevant feature was the large dependence emphasis's high and low pass wavelet filter (derived gray level dependence matrix features). Random forest classifier differentiated PD with an AUC of 0.731, with the most relevant feature being the surface-to-volume ratio. There was no difference in radiomics among the three groups at baseline; therefore, a response could not be predicted. CONCLUSION Radiomics of liver metastases with semi-automatic segmentation demonstrate differences between SD from PR and PD. SUMMARY STATEMENT Semiautomatic segmentation and radiomics of metastatic liver disease demonstrate differences in SD from the PR and progressive metastatic on the baseline and follow-up CT. Despite substantial variations in the scanners, acquisition, and reconstruction parameters, radiomics had an AUC of 0.84-0.89 for differentiating stable hepatic metastases from decreasing and increasing metastatic disease.
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Affiliation(s)
- Leila Mostafavi
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA (L.M., F.H., V.M., G.J.H., M.K.K., S.R.D.); Tumor Imaging Metrics Core (TIMC), Dana-Farber/Harvard Cancer Center, Boston, Massachusetts, USA (L.M., G.J.H.).
| | - Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA (L.M., F.H., V.M., G.J.H., M.K.K., S.R.D.)
| | - Felix Lades
- Siemens Healthcare GmbH, Forchheim, Germany (F.L.)
| | - Andrew Primak
- Siemens Healthineers, Malvern, Pennsylvania, USA (A.P.)
| | - Victorine Muse
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA (L.M., F.H., V.M., G.J.H., M.K.K., S.R.D.)
| | - Gordon J Harris
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA (L.M., F.H., V.M., G.J.H., M.K.K., S.R.D.); Tumor Imaging Metrics Core (TIMC), Dana-Farber/Harvard Cancer Center, Boston, Massachusetts, USA (L.M., G.J.H.)
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA (L.M., F.H., V.M., G.J.H., M.K.K., S.R.D.)
| | - Subba R Digumarthy
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA (L.M., F.H., V.M., G.J.H., M.K.K., S.R.D.)
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Cherezov D, Viswanathan VS, Fu P, Gupta A, Madabhushi A. Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107990. [PMID: 38194767 PMCID: PMC10872259 DOI: 10.1016/j.cmpb.2023.107990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/12/2023] [Accepted: 12/19/2023] [Indexed: 01/11/2024]
Abstract
BACKGROUND Radiomics is a method within medical image analysis that involves the extraction of quantitative data from radiologic scans, often in conjunction with machine learning algorithms to phenotype disease appearance, prognosticate disease outcome, and predict treatment response. However, variance in CT scanner acquisition parameters, such as convolution kernels or pixel spacing, can impact radiomics texture feature values. PURPOSE The extent to which the parameters influence radiomics features continues to be an active area of investigation. In this study, we describe a novel approach, Acquisition Impact on Radiomics Estimation (AcquIRE), to rank the impact of CT acquisition parameters on radiomic texture features. METHODS In this work, we used three chest CT imaging datasets (n = 749 patients) from nine sites comprising: i) lung granulomas and adenocarcinomas (D1) (10 and 52 patients, respectively); ii) minimal and frank invasive adenocarcinoma (D2) (74 and 145 patients); and iii) early-stage NSCLC patients (D3) (315 patients). Datasets D2 and D3 were collected from four sites each, and D1 from a single site. For each patient, 744 texture features and nine acquisition parameters were extracted and utilized to evaluate which parameters impact radiomic features the most. The AcquIRE method establishes a relative assessment between acquisition parameters and radiomic texture featuresa through the creation of a classification model, which is then utilized to assess the rank of the acquisition parameters. RESULTS Across the use cases, CT software version and convolution kernel parameters were found to have the most variance. In D1, it was observed that the Haralick texture feature family was the least affected by variations in acquisition parameters, while the Gabor feature family was the most impacted. However, in datasets D2 and D3, the Gabor features were found to be the least affected. Our findings suggest that the impact on radiomic parameters is as much a function of the problem in question as it is acquisition parameters. CONCLUSIONS The software version and convolution kernel parameters impacted the radiomics feature the most.
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Affiliation(s)
- Dmitry Cherezov
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
| | | | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA; Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
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Wang TW, Chao HS, Chiu HY, Lu CF, Liao CY, Lee Y, Chen JR, Shiao TH, Chen YM, Wu YT. Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors. Transl Oncol 2024; 39:101826. [PMID: 37984256 PMCID: PMC10689936 DOI: 10.1016/j.tranon.2023.101826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/18/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Epidermal growth factor receptor (EGFR)-targeted tyrosine kinase inhibitors (TKIs) are the first-line therapy for EGFR-mutant non-small-cell lung cancer (NSCLC). Early prediction of treatment failure in patients with brain metastases treated with EGFR-TKIs may help in making decisions for systemic drug therapy or local brain tumor control. This study examined the predictive power of the radiomics of both brain metastasis tumors and primary lung tumors. We propose a deep learning based CoxCC model based on quantitative brain magnetic resonance imaging (MRI), a prognostic index and clinical data; the model can be used to predict progression-free survival (PFS) after EGFR-TKI therapy in advanced EGFR-mutant NSCLC. METHODS This retrospective single-center study included 271 patients receiving first-line EGFR-TKI targeted therapy in 2018-2019. Among them, 72 patients who had brain metastases before receiving first-line EGFR-TKI treatment. Three radiomic features were extracted from pretreatment brain MRI images. A CoxCC model for the progression risk stratification of EGFR-TKI treatment was proposed on the basis of MRI radiomics, clinical features, and a prognostic index. We performed time-dependent PFS predictions to evaluate the performance of the CoxCC model. RESULTS The CoxCC model based on a prognostic index, clinical features, and radiomic features of brain metastasis exhibited higher performance than clinical features combined with indexes previously proposed for determining the prognosis of brain metastasis, including recursive partitioning analysis, diagnostic-specific graded prognostic assessment, graded prognostic assessment for lung cancer using molecular markers (lung-molGPA), and modified lung-molGPA, with c-index values of 0.75, 0.67, 0.66, 0.65, and 0.65, respectively. The model achieved areas under the curve of 0.88, 0.73, 0.92, and 0.90 for predicting PFS at 3, 6, 9 and 12 months, respectively. PFS significantly differed between the high- and low-risk groups (p < 0.001). CONCLUSIONS For patients with advanced-stage NSCLC with brain metastasis, MRI radiomics of brain metastases may predict PFS. The CoxCC model integrating brain metastasis radiomics, clinical features, and a prognostic index provided reliable multi-time-point PFS predictions for patients with advanced NSCLC and brain metastases receiving EGFR-TKI treatment.
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Hwa-Yen Chiu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jyun-Ru Chen
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Zhang D, Luan J, Liu B, Yang A, Lv K, Hu P, Han X, Yu H, Shmuel A, Ma G, Zhang C. Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme. Front Med (Lausanne) 2023; 10:1271687. [PMID: 38098850 PMCID: PMC10720716 DOI: 10.3389/fmed.2023.1271687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/15/2023] [Indexed: 12/17/2023] Open
Abstract
Objective To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients. Methods 131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index. Results After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560). Conclusion This study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.
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Affiliation(s)
- Di Zhang
- Department of Radiology, Liaocheng People’s Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China
| | - Jixin Luan
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Aocai Yang
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Kuan Lv
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Pianpian Hu
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People’s Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China
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Wang TW, Chao HS, Chiu HY, Lin YH, Chen HC, Lu CF, Liao CY, Lee Y, Shiao TH, Chen YM, Huang JW, Wu YT. Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients. Cancers (Basel) 2023; 15:5125. [PMID: 37958300 PMCID: PMC10647242 DOI: 10.3390/cancers15215125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.
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Affiliation(s)
- Ting-Wei Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Hwa-Yen Chiu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Yi-Hui Lin
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Hung-Chun Chen
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yen Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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Ikeda R, Kadoya N, Nakajima Y, Ishii S, Shibu T, Jingu K. Impact of CT scan parameters on deformable image registration accuracy using deformable thorax phantom. J Appl Clin Med Phys 2023; 24:e13917. [PMID: 36840512 PMCID: PMC10161117 DOI: 10.1002/acm2.13917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 11/16/2022] [Accepted: 01/05/2023] [Indexed: 02/26/2023] Open
Abstract
The purpose of this study was to evaluate the deformable image registration (DIR) accuracy using various CT scan parameters with deformable thorax phantom. Our developed deformable thorax phantom (Dephan, Chiyoda Technol Corp, Tokyo, Japan) was used. The phantom consists of a base phantom, an inner phantom, and a motor-derived piston. The base phantom is an acrylic cylinder phantom with a diameter of 180 mm, which simulates the chest wall. The inner phantom consists of deformable, 20 mm thick disk-shaped sponges with 48 Lucite beads and 48 nylon cross-wires which simulate the vascular and bronchial bifurcations of the lung. Peak-exhale and peak-inhale images of the deformable phantom were acquired using a CT scanner (Aquilion LB, TOSHIBA). To evaluate the impact of CT scan parameters on DIR accuracy, we used the four tube voltages (80, 100, 120, and 135 kV) and six reconstruction algorithms (FC11, FC13, FC15, FC41, FC44, and FC52). Intensity-based DIR was performed between the two images using MIM Maestro (MIM software, Cleveland, USA). Fiducial markers (beads and cross-wires) based target registration error (TRE) was used for quantitative evaluation of DIR. In case with different tube voltages, the range of average TRE were 4.44-5.69 mm (reconstruction algorithm: FC13). In case with different reconstruction algorithms, the range of average TRE were 4.26-4.59 mm (tube voltage: 120 kV). The TRE were differed by up to 3.0 mm (3.96-6.96 mm) depending on the combination of tube voltage and reconstruction algorithm. Our result indicated that CT scan parameters had moderate impact of TRE, especially for reconstruction algorithms for the deformable thorax phantom.
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Affiliation(s)
- Ryutaro Ikeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
- Department of Radiology, Japanese Red Cross Ishinomaki Hospital, Ishinomaki, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yujiro Nakajima
- Department of Radiological Science, Komazawa University, Tokyo, Japan
| | - Shin Ishii
- Department of Radiology, Japanese Red Cross Ishinomaki Hospital, Ishinomaki, Japan
| | - Takayuki Shibu
- Department of Radiology, Japanese Red Cross Ishinomaki Hospital, Ishinomaki, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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Li Y, Liu J, Yang X, Xu F, Wang L, He C, Lin L, Qing H, Ren J, Zhou P. Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma. LA RADIOLOGIA MEDICA 2023; 128:191-202. [PMID: 36637740 DOI: 10.1007/s11547-023-01591-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023]
Abstract
PURPOSE Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists. MATERIALS AND METHODS A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity. RESULTS No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05). CONCLUSIONS The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.
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Affiliation(s)
- Yong Li
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Fuyang Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Lu Wang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Changjiu He
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Libo Lin
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China.
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9
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Zhao X, Zhao Y, Zhang J, Zhang Z, Liu L, Zhao X. Predicting PD-L1 expression status in patients with non-small cell lung cancer using [ 18F]FDG PET/CT radiomics. EJNMMI Res 2023; 13:4. [PMID: 36682020 PMCID: PMC9868196 DOI: 10.1186/s13550-023-00956-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/17/2023] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND In recent years, immune checkpoint inhibitor (ICI) therapy has greatly changed the treatment prospects of patients with non-small cell lung cancer (NSCLC). Among the available ICI therapy strategies, programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors are the most widely used worldwide. At present, immunohistochemistry (IHC) is the main method to detect PD-L1 expression levels in clinical practice. However, given that IHC is invasive and cannot reflect the expression of PD-L1 dynamically and in real time, it is of great clinical significance to develop a new noninvasive, accurate radiomics method to evaluate PD-L1 expression levels and predict and filter patients who will benefit from immunotherapy. Therefore, the aim of our study was to assess the predictive power of pretherapy [18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics features for PD-L1 expression status in patients with NSCLC. METHODS A total of 334 patients with NSCLC who underwent [18F]FDG PET/CT imaging prior to treatment were analyzed retrospectively from September 2016 to July 2021. The LIFEx7.0.0 package was applied to extract 63 PET and 61 CT radiomics features. In the training group, the least absolute shrinkage and selection operator (LASSO) regression model was employed to select the most predictive radiomics features. We constructed and validated a radiomics model, clinical model and combined model. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate the predictive performance of the three models in the training group and validation group. In addition, a radiomics nomogram to predict PD-L1 expression status was established based on the optimal predictive model. RESULTS Patients were randomly assigned to a training group (n = 233) and a validation group (n = 101). Two radiomics features were selected to construct the radiomics signature model. Multivariate analysis showed that the clinical stage (odds ratio [OR] 1.579, 95% confidence interval [CI] 0.220-0.703, P < 0.001) was a significant predictor of different PD-L1 expression statuses. The AUC of the radiomics model was higher than that of the clinical model in the training group (0.706 vs. 0.638) and the validation group (0.761 vs. 0.640). The AUCs in the training group and validation group of the combined model were 0.718 and 0.769, respectively. CONCLUSION PET/CT-based radiomics features demonstrated strong potential in predicting PD-L1 expression status and thus could be used to preselect patients who may benefit from PD-1/PD-L1-based immunotherapy.
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Affiliation(s)
- Xiaoqian Zhao
- grid.452582.cDepartment of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011 Hebei China
| | - Yan Zhao
- grid.452582.cDepartment of Oncology, The Fourth Hospital of Hebei Medical University and Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei China ,grid.452582.cDepartment of Tumor Immunotherapy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei China
| | - Jingmian Zhang
- grid.452582.cDepartment of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011 Hebei China ,Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei China
| | - Zhaoqi Zhang
- grid.452582.cDepartment of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011 Hebei China
| | - Lihua Liu
- grid.452582.cDepartment of Tumor Immunotherapy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei China
| | - Xinming Zhao
- grid.452582.cDepartment of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011 Hebei China ,Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei China
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10
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Zhang X, Lu B, Yang X, Lan D, Lin S, Zhou Z, Li K, Deng D, Peng P, Zeng Z, Long L. Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature. Eur Radiol 2023; 33:825-835. [PMID: 36166088 PMCID: PMC9889474 DOI: 10.1007/s00330-022-09123-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 08/05/2022] [Accepted: 08/19/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To evaluate the value of time-serial CT radiomics features in predicting progression-free survival (PFS) for lung adenocarcinoma (LUAD) patients after epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) therapy. MATERIALS AND METHODS LUAD patients treated with EGFR-TKIs were retrospectively included from three independent institutes and divided into training and validation cohorts. Intratumoral and peritumoral features were extracted from time-serial non-contrast chest CT (including pre-therapy and first follow-up images); moreover, the percentage variation per unit time (day) was introduced to adjust for the different follow-up periods of each patient. Test-retest was performed to exclude irreproducible features, while the Boruta algorithm was used to select critical radiomics features. Radiomics signatures were constructed with random forest survival models in the training cohort and compared against baseline clinical characteristics through Cox regression and nonparametric testing of concordance indices (C-indices). RESULTS The training cohort included 131 patients (74 women, 56.5%) from one institute and the validation cohort encompassed 41 patients (24 women, 58.5%) from two other institutes. The optimal signature contained 10 features and 7 were unit time feature variations. The comprehensive radiomics model outperformed the pre-therapy clinical characteristics in predicting PFS (training: 0.78, 95% CI: [0.72, 0.84] versus 0.55, 95% CI: [0.49, 0.62], p < 0.001; validation: 0.72, 95% CI: [0.60, 0.84] versus 0.54, 95% CI: [0.42, 0.66], p < 0.001). CONCLUSION Radiomics signature derived from time-serial CT images demonstrated optimal prognostic performance of disease progression. This dynamic imaging biomarker holds the promise of monitoring treatment response and achieving personalized management. KEY POINTS • The intrinsic tumor heterogeneity can be highly dynamic under the therapeutic effect of EGFR-TKI treatment, and the inevitable development of drug resistance may disrupt the duration of clinical benefit. Decision-making remained challenging in practice to detect the emergence of acquired resistance during the early response phase. • Time-serial CT-based radiomics signature integrating intra- and peritumoral features offered the potential to predict progression-free survival for LUAD patients treated with EGFR-TKIs. • The dynamic imaging signature allowed for prognostic risk stratification.
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Affiliation(s)
- Xiaobo Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Bingfeng Lu
- Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | - Xinguan Yang
- Department of Radiology, Guilin People’s Hospital, Guilin, Guangxi China
| | - Dong Lan
- Department of Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | | | - Zhipeng Zhou
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi China
| | - Kai Li
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Dong Deng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Peng Peng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Zisan Zeng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
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11
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Nie K, Xiao Y. Radiomics in clinical trials: perspectives on standardization. Phys Med Biol 2022; 68. [PMID: 36384049 DOI: 10.1088/1361-6560/aca388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/16/2022] [Indexed: 11/17/2022]
Abstract
The term biomarker is used to describe a biological measure of the disease behavior. The existing imaging biomarkers are associated with the known tissue biological characteristics and follow a well-established roadmap to be implemented in routine clinical practice. Recently, a new quantitative imaging analysis approach named radiomics has emerged. It refers to the extraction of a large number of advanced imaging features with high-throughput computing. Extensive research has demonstrated its value in predicting disease behavior, progression, and response to therapeutic options. However, there are numerous challenges to establishing it as a clinically viable solution, including lack of reproducibility and transparency. The data-driven nature also does not offer insights into the underpinning biology of the observed relationships. As such, additional effort is needed to establish it as a qualified biomarker to inform clinical decisions. Here we review the technical difficulties encountered in the clinical applications of radiomics and current effort in addressing some of these challenges in clinical trial designs. By addressing these challenges, the true potential of radiomics can be unleashed.
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Affiliation(s)
- Ke Nie
- Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Department of Radiation Oncology, New Brunswick, NJ, 08901, United States of America
| | - Ying Xiao
- University of Pennsylvania, Department of Radiation Oncology, 3400 Civic Center Blvd, TRC-2 West Philadelphia, PA 19104, United States of America
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12
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A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques. Healthcare (Basel) 2022; 10:healthcare10102075. [DOI: 10.3390/healthcare10102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/17/2022] Open
Abstract
An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.
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13
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Wang Q, Xu S, Zhang G, Zhang X, Gu J, Yang S, Zeng M, Zhang Z. Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis. J Appl Clin Med Phys 2022; 23:e13759. [PMID: 35998185 DOI: 10.1002/acm2.13759] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 07/26/2022] [Accepted: 08/03/2022] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules. MATERIALS AND METHODS CT images of 102 patients, with a total of 118 pulmonary nodules (52 benign, 66 malignant) were retrospectively reconstructed with a deep-learning reconstruction (artificial intelligence iterative reconstruction [AIIR]) and a hybrid iterative reconstruction (HIR) technique. The AIIR data were divided into a training (n = 96) and a validation set (n = 22), and the HIR data were set as the test set (n = 118). Extracted texture features were compared using the Mann-Whitney U test and t-test. The diagnostic performance of the classification model was analyzed with the receiver operating characteristic curve (ROC), the area under ROC (AUC), sensitivity, specificity, and accuracy. RESULTS Among the obtained 68 texture features, 51 (75.0%) were not influenced by the change of reconstruction algorithm (p > 0.05). Forty-four features were significantly different between benign and malignant nodules (p < 0.05) for the AIIR dataset, which were selected to build the classification model. The accuracy and AUC of the classification model were 92.3% and 0.91 (95% confidence interval [CI], 0.74-0.90) with the validation set, which were 80.0% and 0.80 (95% CI, 0.68-0.86) with the test set. CONCLUSION With the CT texture analysis model trained with deep-learning reconstruction (AIIR) images showing favorable diagnostic accuracy in discriminating benign and malignant pulmonary nodules, it also has certain applicability to the iterative reconstruction (HIR) images.
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Affiliation(s)
- Qingle Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shijie Xu
- Shanghai United Imaging Healthcare, Shanghai, China
| | - Guozhi Zhang
- Shanghai United Imaging Healthcare, Shanghai, China
| | - Xingwei Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, China
| | - Junying Gu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shuyi Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhiyong Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, China
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14
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Gao Y, Hua M, Lv J, Ma Y, Liu Y, Ren M, Tian Y, Li X, Zhang H. Reproducibility of radiomic features of pulmonary nodules between low-dose CT and conventional-dose CT. Quant Imaging Med Surg 2022; 12:2368-2377. [PMID: 35371962 PMCID: PMC8923849 DOI: 10.21037/qims-21-609] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/30/2021] [Indexed: 09/25/2023]
Abstract
BACKGROUND The reproducibility of radiomic features is essential to lung cancer detection. This study aimed to investigate the reproducibility of radiomic features of pulmonary nodules between low-dose computed tomography (LDCT) and conventional-dose computed tomography (CDCT). METHODS A total of 105 patients with 119 pulmonary nodules [39 ground-glass nodules (GGNs) and 80 solid nodules] who underwent LDCT and CDCT were retrospectively studied between September 2019 and November 2020. Pulmonary nodules were manually segmented and 1,125 radiomic features (shape, first-order intensity, texture, wavelet, and Laplacian of the Gaussian features) were extracted from both LDCT and CDCT images. The concordance correlation coefficient (CCC) was used to evaluate the reproducibility of these radiomic features. RESULTS Of the 1,125 radiomic features considered, 35.5% (399 of 1,125) and 41.5% (467 of 1,125) were reproducible (CCC ≥0.85) for GGNs and solid nodules, respectively. The intensity, texture, and wavelet features of solid nodules were more reproducible than those of GGNs. The mean CCC values for intensity and texture features of solid nodules were of 0.85 and above, whereas the mean values for those of GGNs were of less than 0.85. After Gaussian kernel (σ =2) preprocessing, the CCC of intensity and texture features of GGNs improved from 0.77 to 0.90, and 84.9% (79 of 93) of the radiomic features were reproducible (mean CCC increase from 0.84±0.13 to 0.92±0.08 for intensity features, and from 0.75±0.15 to 0.89±0.11 for texture features). Wavelet features had the lowest CCCs for both GGNs and solid nodules. CONCLUSIONS The majority of the radiomic feature classes of solid pulmonary nodules have a high level of reproducibility between LDCT and CDCT. However, LDCT should not be used as an alternative to CDCT in the radiomic study of GGNs.
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Affiliation(s)
- Yufan Gao
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Minghui Hua
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Jun Lv
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Yanhe Ma
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Yanzhen Liu
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Min Ren
- Tianjin Cardiovascular Institute, Chest Hospital, Tianjin University, Tianjin, China
| | - Yaohua Tian
- Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ximing Li
- Department of Cardiovascular Medicine, Chest Hospital, Tianjin University, Tianjin, China
| | - Hong Zhang
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
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15
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Li Y, Reyhan M, Zhang Y, Wang X, Zhou J, Zhang Y, Yue NJ, Nie K. The impact of phantom design and material-dependence on repeatability and reproducibility of CT-based radiomics features. Med Phys 2022; 49:1648-1659. [PMID: 35103332 DOI: 10.1002/mp.15491] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/26/2021] [Accepted: 12/29/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE To understand the design of radiomics phantom and material-dependence on repeatability and reproducibility of CT radiomics features METHODS: : A radiomics phantom consisting of various materials with uniformity, textural and biological components, was constructed. The phantom was scanned with different manufacturer CT scanners and the scans were repeated multiple times on the same scanner with different acquisition settings as kVp, mAs, orientation, field of view (FOV), slice thickness, pitch, reconstruction kernels and acquisition mode. A total of 72 phantom scans were included. For each scan, 18 different regions of interest (ROI) were contoured and 708 radiomics features were extracted from each ROI via an open source radiomics tool, IBEX. To relate the phantom data to patient data, the radiomics features from different phantom materials were compared with those extracted from 50 patients' images of five disease sites as brain, head-and-neck, breast, liver and lung cases using box-plots comparison and principal component analysis (PCA). The temporal stability of imaging features was then evaluated with respect to a controlled scenario (test-retest) via the intra-class correlation coefficient (ICC). The reproducibility of radiomics features with respect to different scanners or acquisition settings were further evaluated with concordance correlation coefficients (CCC). RESULTS Among all phantom materials, the biological component had feature values closest to human tissues, especially for tumors in brain and liver. The textural component showed similar ranges of variation to lung lesions, particularly for cartridges of rice, cereal, and the 3D-printed textural phantom with fine and rough-grid. It also showed that certain materials, such as polystyrene foam, plaster and peanuts, did not have comparable values to human tissue and could be excluded for future phantom design. High repeatability was observed in the test-retest study as indicated by an ICC value of 0.998 ± 0.020. All materials were used for feature stability analysis. For the inter-scanner study, shape-related features were the most-reliable category with 94% of features having CCC ≥0.9, while GOH were the least-reliable with only 14.6% meeting the criteria. For the intra-scanner study, the reproducibility of CT-based radiomics features showed material-dependence. In general, the instability of radiomics features introduced by kVp, mAs, pitch, acquisition mode and orientation were relatively mild. However, the homogeneous materials were more vulnerable to those changes compared to materials with textural patterns. Regardless of material compositions, resolution parameters like FOV and slice thickness, could have large impact on feature stability. Switching between standard and bone reconstruction kernels could also result significant changes to feature reproducibility. CONCLUSION We have built a radiomics phantom using materials that cover a wide span of tumor textures seen in oncological CT images. The designed phantom presents a preliminary opportunity for investigating reproducibility of radiomics features and the reproducibility can be material dependent. Thus, in the radiomics quality assurance design, it is important to choose appropriate materials that can provide a close range of radiomics features to patients with specific disease sites dependency taken into consideration. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yanjing Li
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Meral Reyhan
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Yin Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Jinghao Zhou
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Ning J Yue
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
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16
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Abstract
The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection.
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17
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Adachi T, Nagasawa R, Nakamura M, Kakino R, Mizowaki T. Vulnerabilities of radiomic features to respiratory motion on four-dimensional computed tomography-based average intensity projection images: A phantom study. J Appl Clin Med Phys 2022; 23:e13498. [PMID: 35088515 PMCID: PMC8906211 DOI: 10.1002/acm2.13498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 05/11/2021] [Accepted: 09/11/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To evaluate the influence of respiratory motion on the robustness of radiomic features on four-dimensional computed tomography (4DCT)-based average intensity projection (AIP) images by employing an anthropomorphic chest phantom. METHODS Three spherical objects (φ30 mm), namely, acrylic (100 Hounsfield unit [HU], homogeneous), rubber (-140 HU, homogeneous), and cork (-630 HU, heterogeneous), were moved with motion amplitudes of 0, 1, 2.5, 4, 6, 8, and 10 mm in the phantom, and 4DCT scans were repeated at four different locations. Thereafter, the AIP images were generated considering the average of the 10 respiratory phases of the 4DCT images. Further, the targets were manually delineated on the AIP images in the lung window setting. A total of 851 radiomic features, including 107 unfiltered features and 744 wavelet filter-based features, were extracted from the region of interest for each material. The feature robustness among the different target motion amplitude (ε) was evaluated by normalizing the feature variability of the target motion relative to the variability of data from 573 patients with early-stage non-small cell lung cancer. The features with absolute ε values ≤0.5 were considered highly robust to target motions. RESULTS The percentage of robust unfiltered and wavelet filter-based features with a motion amplitude of 1 mm was greater than 83.2% and 93.4%, respectively; however, the percentage decreased by more than 24.3% and 17.6%, respectively, for motion amplitudes greater than 2.5 mm. The movement of cork had a small effect on the feature robustness compared to that of acrylic and rubber, regardless of the target motion amplitudes. CONCLUSIONS Our phantom study demonstrated that target motion amplitudes ≤1 mm led to the robustness of radiomic features on the 4DCT-based AIP images of thoracic regions. The frequency components and directions of the wavelet filters may be essential factors in 4DCT-based radiomic analysis.
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Affiliation(s)
- Takanori Adachi
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Sakyo-ku, Japan.,Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Sakyo-ku, Japan
| | - Ryoko Nagasawa
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Sakyo-ku, Japan
| | - Mitsuhiro Nakamura
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Sakyo-ku, Japan.,Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Sakyo-ku, Japan
| | - Ryo Kakino
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Sakyo-ku, Japan.,Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Sakyo-ku, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Sakyo-ku, Japan
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18
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Shiinoki T, Fujimoto K, Kawazoe Y, Yuasa Y, Kajima M, Manabe Y, Ono T, Hirano T, Matsunaga K, Tanaka H. Predicting programmed death-ligand 1 expression level in non-small cell lung cancer using a combination of peritumoral and intratumoral radiomic features on computed tomography. Biomed Phys Eng Express 2022; 8. [PMID: 35051908 DOI: 10.1088/2057-1976/ac4d43] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/20/2022] [Indexed: 12/24/2022]
Abstract
In this study, we investigated the possibility of predicting expression levels of programmed death-ligand 1 (PD-L1) using radiomic features of intratumoral and peritumoral tumors on computed tomography (CT) images. We retrospectively analyzed 161 patients with non-small cell lung cancer. We extracted radiomics features for intratumoral and peritumoral regions on CT images. The null importance, least absolute shrinkage, and selection operator model were used to select the optimized feature subset to build the prediction models for the PD-L1 expression level. LightGBM with five-fold cross-validation was used to construct the prediction model and evaluate the receiver operating characteristics. The corresponding area under the curve (AUC) was calculated for the training and testing cohorts. The proportion of ambiguously clustered pairs was calculated based on consensus clustering to evaluate the validity of the selected features. In addition, Radscore was calculated for the training and test cohorts. For expression level of PD-L1 above 1%, prediction models that included radiomic features from the intratumoral region and a combination of radiomic features from intratumoral and peritumoral regions yielded an AUC of 0.83 and 0.87 and 0.64 and 0.74 in the training and test cohorts, respectively. In contrast, the models above 50% prediction yielded an AUC of 0.80, 0.97, and 0.74, 0.83, respectively. The selected features were divided into two subgroups based on PD-L1 expression levels ≥ 50% or ≥ 1%. Radscore was statistically higher for subgroup one than subgroup two when radiomic features for intratumoral and peritumoral regions were combined. We constructed a predictive model for PD-L1 expression level using CT images. The model using a combination of intratumoral and peritumoral radiomic features had a higher accuracy than the model with only intratumoral radiomic features.
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Affiliation(s)
- Takehiro Shiinoki
- Department of Radiation Oncology, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
| | - Koya Fujimoto
- Department of Radiation Oncology, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
| | - Yusuke Kawazoe
- Department of Radiation Oncology, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
| | - Yuki Yuasa
- Department of Radiation Oncology, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
| | - Miki Kajima
- Department of Radiation Oncology, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
| | - Yuki Manabe
- Department of Radiation Oncology, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
| | - Taiki Ono
- Department of Radiation Oncology, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
| | - Tsunahiko Hirano
- Department of Respiratory Medicine and Infectious Disease, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
| | - Kazuto Matsunaga
- Department of Respiratory Medicine and Infectious Disease, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
| | - Hidekazu Tanaka
- Department of Radiation Oncology, Yamaguchi University, Minamikogushi 1-1-1, Ube, Yamaguchi, 7558505, JAPAN
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Moradmand H, Aghamiri SMR, Ghaderi R, Emami H. The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma. Cancer Med 2021; 10:7048-7059. [PMID: 34453413 PMCID: PMC8525162 DOI: 10.1002/cam4.4230] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/30/2021] [Accepted: 08/01/2021] [Indexed: 11/25/2022] Open
Abstract
This retrospective study has been conducted to validate the performance of deep learning‐based survival models in glioblastoma (GBM) patients alongside the Cox proportional hazards model (CoxPH) and the random survival forest (RSF). Furthermore, the effect of hyperparameters optimization methods on improving the prediction accuracy of deep learning‐based survival models was investigated. Of the 305 cases, 260 GBM patients were included in our analysis based on the following criteria: demographic information (i.e., age, Karnofsky performance score, gender, and race), tumor characteristic (i.e., laterality and location), details of post‐surgical treatment (i.e., time to initiate concurrent chemoradiation therapy, standard treatment, and radiotherapy techniques), and last follow‐up time as well as the molecular markers (i.e., O‐6‐methylguanine methyltransferase and isocitrate dehydrogenase 1 status). Experimental results have demonstrated that age (Elderly > 65: hazard ratio [HR] = 1.63; 95% confidence interval [CI]: 1.213–2.18; p value = 0.001) and tumors located at multiple lobes ([HR] = 1.75; 95% [CI]: 1.177–2.61; p value = 0.006) were associated with poorer prognosis. In contrast, age (young < 40: [HR] = 0.57; 95% [CI]: 0.343–0.96; p value = 0.034) and type of radiotherapy (others include stereotactic and brachytherapy: [HR] = 0.5; 95%[CI]: 0.266–0.95; p value = 0.035) were significantly related to better prognosis. Furthermore, the proposed deep learning‐based survival model (concordance index [c‐index] = 0.823 configured by Bayesian hyperparameter optimization), outperformed the RSF (c‐index = 0.728), and the CoxPH model (c‐index = 0.713) in the training dataset. Our results show the ability of deep learning in learning a complex association of risk factors. Moreover, the remarkable performance of the deep‐learning‐based survival model could be promising to support decision‐making systems in personalized medicine for patients with GBM.
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Affiliation(s)
- Hajar Moradmand
- Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
| | | | - Reza Ghaderi
- Electrical Engineering, Shahid Beheshti University, Tehran, Iran
| | - Hamid Emami
- Department of Radiation Oncology, Isfahan University of Medical Sciences, Seyed Al-Shohada Charity Hospital, Isfahan, Iran
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20
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Ye K, Chen M, Zhu Q, Lu Y, Yuan H. Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules. Quant Imaging Med Surg 2021; 11:2344-2353. [PMID: 34079706 DOI: 10.21037/qims-20-932] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background The weightings of iterative reconstruction algorithm can affect CT radiomic quantification. But, the effect of ASiR-V levels on the reproducibility of CT radiomic features between ultra-low-dose computed tomography (ULDCT) and low-dose computed tomography (LDCT) is still unknown. The purpose of study is to investigate whether adaptive statistical iterative reconstruction-V (ASiR-V) levels affect radiomic feature quantification using ULDCT and to assess the reproducibility of radiomic features between ULDCT and LDCT. Methods Sixty-three patients with pulmonary nodules underwent LDCT (0.70±0.16 mSv) and ULDCT (0.15±0.02 mSv). LDCT was reconstructed with ASiR-V 50%, and ULDCT with ASiR-V 50%, 70%, and 90%. Radiomics analysis was applied, and 107 features were extracted. The concordance correlation coefficient (CCC) was calculated to describe agreement among ULDCTs and between ULDCT and LDCT for each feature. The proportion of features with CCC >0.9 among ULDCTs and between ULDCT and LDCT, and the mean CCC for all features between ULDCT and LDCT were also compared. Results Sixty-three solid nodules (SNs) and 48 pure ground-glass nodules (pGGNs) were analyzed. There was no difference for the proportion of features in SNs among ULDCTs and between ULDCT and LDCT (P>0.05). The proportion of features in pGGNs were highest for ULDCT70% vs. 90% (78.5%) and ULDCT90% vs. LDCT50% (50.5%). In SNs, the mean CCC for ULDCT90% vs. LDCT50% was 0.67±0.26, not different with that for ULDCT50% vs. LDCT50% (0.68±0.24) and ULDCT70% vs. LDCT50% (0.64±0.21) (P>0.05). In pGGNs, the mean CCC for ULDCT90% vs. LDCT50% was 0.79±0.19, higher than that for ULDCT50% vs. LDCT50% (0.61±0.28) and ULDCT70% vs. LDCT50% (0.76±0.24) (P<0.05). Conclusions ASiR-V levels significantly affected ULDCT radiomic feature quantification in pulmonary nodules, with stronger effects in pGGNs than in SNs. The reproducibility of radiomic features was highest between ULDCT90% and LDCT50%.
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Affiliation(s)
- Kai Ye
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Min Chen
- Department of Radiology, Ghent University Hospital, Corneel Heymanslaan 10,9000, Ghent, Belgium
| | - Qiao Zhu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yuliu Lu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
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Emaminejad N, Wahi-Anwar MW, Kim GHJ, Hsu W, Brown M, McNitt-Gray M. Reproducibility of lung nodule radiomic features: Multivariable and univariable investigations that account for interactions between CT acquisition and reconstruction parameters. Med Phys 2021; 48:2906-2919. [PMID: 33706419 PMCID: PMC8273077 DOI: 10.1002/mp.14830] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/01/2021] [Accepted: 02/23/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Recent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel. METHOD A cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer-aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6 mm, 1 mm, and 2 mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well-known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC ≥ 0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA. RESULTS All included radiomic features lacked intercondition reproducibility (OCCC < 0.9) among all the 36 conditions. Out of 226 radiomic features analyzed, only 17 and 18 features were considered reproducible (OCCC ≥ 0.9) to dose and kernel variation, respectively, within the corresponding condition subsets. Slice thickness demonstrated the largest impact on radiomic feature values where only one to five features were reproducible at a few condition subsets. ANOVA revealed significant interactions (P < 0.05) between CT parameters affecting the variability of >50% of radiomic features. CONCLUSION We systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies.
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Affiliation(s)
- Nastaran Emaminejad
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA
| | | | - Grace Hyun J. Kim
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA
| | - William Hsu
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA
| | - Matthew Brown
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA
| | - Michael McNitt-Gray
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA
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Reiazi R, Abbas E, Famiyeh P, Rezaie A, Kwan JYY, Patel T, Bratman SV, Tadic T, Liu FF, Haibe-Kains B. The impact of the variation of imaging parameters on the robustness of Computed Tomography radiomic features: A review. Comput Biol Med 2021; 133:104400. [PMID: 33930766 DOI: 10.1016/j.compbiomed.2021.104400] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/23/2021] [Accepted: 04/11/2021] [Indexed: 12/13/2022]
Abstract
The field of radiomics is at the forefront of personalized medicine. However, there is concern that high variation in imaging parameters will impact robustness of radiomic features and subsequently the performance of the predictive models built upon them. Therefore, our review aims to evaluate the impact of imaging parameters on the robustness of radiomic features. We also provide insights into the validity and discrepancy of different methodologies applied to investigate the robustness of radiomic features. We selected 47 papers based on our predefined inclusion criteria and grouped these papers by the imaging parameter under investigation: (i) scanner parameters, (ii) acquisition parameters and (iii) reconstruction parameters. Our review highlighted that most of the imaging parameters are disruptive parameters, and shape along with First order statistics were reported as the most robust radiomic features against variation in imaging parameters. This review identified inconsistencies related to the methodology of the reviewed studies such as the metrics used for robustness, the feature extraction techniques, the reporting style, and their outcome inclusion. We hope this review will aid the scientific community in conducting research in a way that is more reproducible and avoids the pitfalls of previous analyses.
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Affiliation(s)
- Reza Reiazi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Engy Abbas
- Joint Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Petra Famiyeh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Aria Rezaie
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Jennifer Y Y Kwan
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Tirth Patel
- Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Scott V Bratman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Tony Tadic
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Fei-Fei Liu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Ontario Institute for Cancer Research, Toronto, Ontario, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada; Vector Institute, Toronto, Ontario, Canada.
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23
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Yang S, Wu N, Zhang L, Li M. Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:279. [PMID: 33708906 PMCID: PMC7944270 DOI: 10.21037/atm-20-2992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Background To investigate the influence of slice thickness on radiomic feature (RF) values in solid pulmonary nodules and evaluate the effect of a linear interpolation method in correcting the influence. Methods Thirty pulmonary nodules from 28 patients were selected prospectively with a thick-slice of 5 mm and a thin-slice of 1.25 mm on CT. A resampling method was used to normalize the voxel size of thick and thin slices CT images to 1×1×1 mm3 by linear interpolation. Lung nodules were segmented manually. A total of 396 radiomic features (RFs) were extracted from thick-slice and thin-slice images, together with the images resampled from thick (thick-r) and thin (thin-r) slices. The differences between the RF values were evaluated using a paired t-test. A comparison between groups was made using the Chi-squared test. Results Among the 396 RFs, 305 RFs showed an intraclass correlation coefficient ≥0.75 after test-retest analysis (including 22 histogram features, 20 geometry features, and 263 texture features). In the non-resampled data, 239 RF values (78.4%, 239/305) showed significant differences between thick and thin slice CT images. Resampling of thick images revealed that 202 RF values (66.2%, 202/305) showed significant differences between thick-r and thin slice CT images, showing a significant decrease in the number of different RF values when compared to non-resampled data (P<0.01). For the RF subgroups, only texture features showed a significant reduction in the number of different RF values after resampling (P<0.01). When both thick and thin slice images were resampled, the number of significantly different RF values between thick-r and thin-r images was increased to 247 (81.0%, 247/305), showing no significant difference when compared to non-resampled data (P=0.421). Conclusions Slice thickness demonstrated a considerable influence on RF values in solid pulmonary nodules, producing false results when CT images with different slice thicknesses were used. Linear interpolation of the resampling method was limited because of the relatively small correction effect.
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Affiliation(s)
- Shouxin Yang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ning Wu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Vonder M, Dorrius MD, Vliegenthart R. Latest CT technologies in lung cancer screening: protocols and radiation dose reduction. Transl Lung Cancer Res 2021; 10:1154-1164. [PMID: 33718053 PMCID: PMC7947397 DOI: 10.21037/tlcr-20-808] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The aim of this review is to provide clinicians and technicians with an overview of the development of CT protocols in lung cancer screening. CT protocols have evolved from pre-fixed settings in early lung cancer screening studies starting in 2004 towards automatic optimized settings in current international guidelines. The acquisition protocols of large lung cancer screening studies and guidelines are summarized. Radiation dose may vary considerably between CT protocols, but has reduced gradually over the years. Ultra-low dose acquisition can be achieved by applying latest dose reduction techniques. The use of low tube current or tin-filter in combination with iterative reconstruction allow to reduce the radiation dose to a submilliSievert level. However, one should be cautious in reducing the radiation dose to ultra-low dose settings since performed studies lacked generalizability. Continuous efforts are made by international radiology organizations to streamline the CT data acquisition and image quality assurance and to keep track of new developments in CT lung cancer screening. Examples like computer-aided diagnosis and radiomic feature extraction are discussed and current limitations are outlined. Deep learning-based solutions in post-processing of CT images are provided. Finally, future perspectives and recommendations are provided for lung cancer screening CT protocols.
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Affiliation(s)
- Marleen Vonder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Monique D Dorrius
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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25
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Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma. Mol Imaging Biol 2020; 22:1581-1591. [DOI: 10.1007/s11307-020-01507-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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26
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Fornacon-Wood I, Faivre-Finn C, O'Connor JPB, Price GJ. Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer 2020; 146:197-208. [PMID: 32563015 PMCID: PMC7383235 DOI: 10.1016/j.lungcan.2020.05.028] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/18/2020] [Accepted: 05/23/2020] [Indexed: 12/24/2022]
Abstract
Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.
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Affiliation(s)
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - James P B O'Connor
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - Gareth J Price
- Division of Cancer Sciences, University of Manchester, Manchester, UK
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27
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Recent developments and advances in secondary prevention of lung cancer. Eur J Cancer Prev 2020; 29:321-328. [PMID: 32452945 DOI: 10.1097/cej.0000000000000586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Lung cancer prevention may include primary prevention strategies, such as corrections of working conditions and life style - primarily smoking cessation - as well as secondary prevention strategies, aiming at early detection that allows better survival rates and limited resections. This review summarizes recent developments and advances in secondary prevention, focusing on recent technological tools for an effective early diagnosis.
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28
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Jiang M, Sun D, Guo Y, Guo Y, Xiao J, Wang L, Yao X. Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result. Acad Radiol 2020; 27:171-179. [PMID: 31147234 DOI: 10.1016/j.acra.2019.04.016] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Revised: 04/12/2019] [Accepted: 04/13/2019] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To explore the potential value of radiomic features-derived approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) patients. MATERIALS AND METHODS A cohort of 399 stage I-IV NSCLC patients were enrolled. Tumor segmentation was performed to select essential primary lesions of NSCLC cases after PET/CT images acquisition. Features were extracted, then filtered with automatic relevance determination and minimized with LASSO model based on its relevance of PD-L1 expression status. Finally, we built predictive models with features from the CT, the PET, and the PET/CT images, respectively, for differentiating different status of specific PD-L1 types. Five-fold cross validation was practiced to evaluate the signatures' accuracy, and the receiver operating characteristic as well as the corresponding area under the curve (AUC) was reckoned for each model. RESULTS With the total of 24 selected features which were significantly associated with PD-L1 expression levels, models based on CT-, PET-, PET/CT-derived features were built and compared. For PD-L1 (SP142) expression level over 1% prediction, models that comprised radiomic features from the CT, the PET, and the PET/CT images resulted in an AUC of 0.97, 0.61, and 0.97, respectively; models for over 50% prediction resulted with AUC of 0.80, 0.65, and 0.77. For PD-L1 (28-8) expression level prediction, predictive models of over 1% expression scored at 0.86, 0.62, and 0.85; and signatures of over 50% expression reached the score of AUCs at 0.91, 0.75, and 0.88, respectively. CONCLUSION The radiomic-based predictive approach, especially CT-derived predictive model, may anticipate PD-L1 expression status in NSCLC patients relatively accurate. It may be helpful in guiding immunotherapy in clinical practice and deserves further analysis.
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Affiliation(s)
- Mengmeng Jiang
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China; Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 180, Fenglin Road, Xuhui District, Shanghai 200032, China
| | - Dazhen Sun
- Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, and Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan, RD. Minhang District, Shanghai, China
| | - Yinglong Guo
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China; Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 180, Fenglin Road, Xuhui District, Shanghai 200032, China
| | - Yixian Guo
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China; Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 180, Fenglin Road, Xuhui District, Shanghai 200032, China
| | - Jie Xiao
- Department of Nuclear Medicine, Zhongshan Hospital of Fudan University, Fudan University, Shanghai, China
| | - Lisheng Wang
- Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, and Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan, RD. Minhang District, Shanghai, China.
| | - Xiuzhong Yao
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China; Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 180, Fenglin Road, Xuhui District, Shanghai 200032, China.
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Ley S, Ley-Zaporozhan J. Novelties in imaging in pulmonary fibrosis and nodules. A narrative review. Pulmonology 2019; 26:39-44. [PMID: 31706882 DOI: 10.1016/j.pulmoe.2019.09.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 09/25/2019] [Indexed: 12/22/2022] Open
Abstract
In recent months two major fields of interest in pulmonary imaging have stood out: pulmonary fibrosis and pulmonary nodules. New guidelines have been released to define pulmonary fibrosis and subsequent studies have proved the value of these changes. In addition, new recommendations for classification of pulmonary nodules have been released. Radiological images are of major interest for automated and standardized analysis and so in both cases software tools using artificial intelligence were developed for visualization and quantification of the disease. These tools have been validated by human readers and demonstrated their capabilities. This review summarizes the new recommendations for classification of pulmonary fibrosis and nodules and reviews the capabilities of radiomics within these two entities.
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Affiliation(s)
- S Ley
- Chirurgisches Klinikum München Süd, Am Isarkanal 30, 81379 München, Germany.
| | - J Ley-Zaporozhan
- Chirurgisches Klinikum München Süd, Am Isarkanal 30, 81379 München, Germany
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Waninger JJ, Green MD, Cheze Le Rest C, Rosen B, El Naqa I. Integrating radiomics into clinical trial design. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 63:339-346. [PMID: 31527581 DOI: 10.23736/s1824-4785.19.03217-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In radiomics, quantitative features that describe phenotypic tumor characteristics are derived from radiographic images. Because radiomics generates information from routine medical images, it is a powerful way to non-invasively examine the spatial and temporal heterogeneity of disease, and thus has potential to significantly impact clinical trial design, execution, and ultimately patient care. The aim of this review article is to discuss how radiomics may address some of the current challenges in clinical randomized control trials, and the difficulties of integrating robust and repeatable radiomics analysis into trial design. Each step of the radiomics process, including image acquisition and reconstruction, image segmentation, feature extraction, and computational analysis, requires extensive standardization in order to be successfully incorporated into clinical trials and inform clinical decision making. By addressing these challenges, the potential of radiomics may be realized.
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Affiliation(s)
- Jessica J Waninger
- Department of Medical Education, University of Michigan School of Medicine, Ann Arbor, MI, USA.,Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Michael D Green
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA.,University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | | | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA -
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