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Cao Y, Zhang J, Huang L, Zhao Z, Zhang G, Ren J, Li H, Zhang H, Guo B, Wang Z, Xing Y, Zhou J. Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics. Jpn J Radiol 2023; 41:1236-1246. [PMID: 37311935 PMCID: PMC10613595 DOI: 10.1007/s11604-023-01458-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/04/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND In this study, we used computed tomography (CT)-based radiomics signatures to predict the mutation status of KRAS in patients with colorectal cancer (CRC) and to identify the phase of radiomics signature with the most robust and high performance from triphasic enhanced CT. METHODS This study involved 447 patients who underwent KRAS mutation testing and preoperative triphasic enhanced CT. They were categorized into training (n = 313) and validation cohorts (n = 134) in a 7:3 ratio. Radiomics features were extracted using triphasic enhanced CT imaging. The Boruta algorithm was used to retain the features closely associated with KRAS mutations. The Random Forest (RF) algorithm was used to develop radiomics, clinical, and combined clinical-radiomics models for KRAS mutations. The receiver operating characteristic curve, calibration curve, and decision curve were used to evaluate the predictive performance and clinical usefulness of each model. RESULTS Age, CEA level, and clinical T stage were independent predictors of KRAS mutation status. After rigorous feature screening, four arterial phase (AP), three venous phase (VP), and seven delayed phase (DP) radiomics features were retained as the final signatures for predicting KRAS mutations. The DP models showed superior predictive performance compared to AP or VP models. The clinical-radiomics fusion model showed excellent performance, with an AUC, sensitivity, and specificity of 0.772, 0.792, and 0.646 in the training cohort, and 0.755, 0.724, and 0.684 in the validation cohort, respectively. The decision curve showed that the clinical-radiomics fusion model had more clinical practicality than the single clinical or radiomics model in predicting KRAS mutation status. CONCLUSION The clinical-radiomics fusion model, which combines the clinical and DP radiomics model, has the best predictive performance for predicting the mutation status of KRAS in CRC, and the constructed model has been effectively verified by an internal validation cohort.
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Affiliation(s)
- Yuntai Cao
- Department of Radiology, Affiliated Hospital of Qinghai University, Tongren Road No. 29, Xining, 810001, People's Republic of China.
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, People's Republic of China.
| | - Jing Zhang
- The Fifth Affiliated Hospital of Zunyi Medical University, Zunyi, 519100, People's Republic of China
| | - Lele Huang
- Department of Nuclear Medicine, Lanzhou University Second Hospital, Lanzhou, China
| | - Zhiyong Zhao
- Department of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, China
| | - Guojin Zhang
- Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China
| | - Hailong Li
- Affiliated Hospital of Qinghai University, Xining, China
| | - Hongqian Zhang
- Affiliated Hospital of Qinghai University, Xining, China
| | - Bin Guo
- Affiliated Hospital of Qinghai University, Xining, China
| | - Zhan Wang
- Affiliated Hospital of Qinghai University, Xining, China
| | - Yue Xing
- Xinxiang Medical University, Henan, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, People's Republic of China.
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Matsuura K, Ichikawa K, Kawashima H. Task-specific spatial resolution properties of iterative and deep learning-based reconstructions in computed tomography: Comparison using tasks assuming small and large enhanced vessels. Phys Med 2022; 95:64-72. [PMID: 35123172 DOI: 10.1016/j.ejmp.2022.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/15/2021] [Accepted: 01/26/2022] [Indexed: 10/19/2022] Open
Abstract
PURPOSE The present study aims to evaluate TTFs of deep-learning-based image reconstruction (DLIR) and iterative reconstruction (IR) in computed tomography (CT) using a conventional task with a rod object with a diameter of 30 mm and a newly-proposed task with a wire of 1 mm in diameter, simulating large and small enhanced vessels, respectively. METHODS The rod or wire phantom made of a material equivalent to diluted iodine that exhibits about 270 Hounsfield unit (HU) was placed inside a 30-cm water phantom. In-plane and z-directional TTFs were measured for the rod using the circular edge (CE) and plane edge (PE) methods, respectively. By using the wire (iodine wire: IW), in-plane and z-directional TTFs were measured using Fourier transform (IW method). TTFs of filtered back projection (FBP), IR, and DLIR of a 256-row CT system and FBP and IR of a 64-row CT system were evaluated with CT dose indices of 10 and 5 mGy. RESULTS For DLIR and IR, TTFs measured using the IW method were notably lower than those using the CE (or PE) method; moreover, they were also lower than those of corresponding FBP, indicating that the small enhanced vessels with a diameter of about 1 mm would be blurred with both DLIR and IR. CONCLUSIONS The proposed IW method has turned out to be effective to evaluate TTFs for small enhanced vessels, which have not been properly evaluated by the CE or PE method conventionally recommended.
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Affiliation(s)
- Kanae Matsuura
- Dept of Radiological Technology, Faculty of Health Science, Suzuka University of Medical Science, 1001-1 Kishioka-cho, Suzuka 510-0293, Japan; Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
| | - Katsuhiro Ichikawa
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
| | - Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
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Cao Y, Zhang J, Bao H, Zhang G, Yan X, Wang Z, Ren J, Chai Y, Zhao Z, Zhou J. Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer. Front Oncol 2021; 11:689176. [PMID: 34631524 PMCID: PMC8493878 DOI: 10.3389/fonc.2021.689176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
Objective This study aimed to develop a dual-energy spectral computed tomography (DESCT) nomogram that incorporated both clinical factors and DESCT parameters for individual preoperative prediction of lymph node metastasis (LNM) in patients with colorectal cancer (CRC). Material and Methods We retrospectively reviewed 167 pathologically confirmed patients with CRC who underwent enhanced DESCT preoperatively, and these patients were categorized into training (n = 117) and validation cohorts (n = 50). The monochromatic CT value, iodine concentration value (IC), and effective atomic number (Eff-Z) of the primary tumors were measured independently in the arterial phase (AP) and venous phase (VP) by two radiologists. DESCT parameters together with clinical factors were input into the prediction model for predicting LNM in patients with CRC. Logistic regression analyses were performed to screen for significant predictors of LNM, and these predictors were presented as an easy-to-use nomogram. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram. Results The logistic regression analysis showed that carcinoembryonic antigen, carbohydrate antigen 199, pericolorectal fat invasion, ICAP, ICVP, and Eff-ZVP were independent predictors in the predictive model. Based on these predictors, a quantitative nomogram was developed to predict individual LNM probability. The area under the curve (AUC) values of the nomogram were 0.876 in the training cohort and 0.852 in the validation cohort, respectively. DCA showed that our nomogram has outstanding clinical utility. Conclusions This study presents a clinical nomogram that incorporates clinical factors and DESCT parameters and can potentially be used as a clinical tool for individual preoperative prediction of LNM in patients with CRC.
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Affiliation(s)
- Yuntai Cao
- Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Jing Zhang
- Department of Radiology, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Haihua Bao
- Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China
| | - Guojin Zhang
- Department of Radiology, Sichuan Provincial People's Hospital, Chengdu, China
| | - Xiaohong Yan
- Department of Critical Medicine, Affiliated Hospital of Qinghai University, Xining, China
| | - Zhan Wang
- Department of Hepatopancreatobiliary Surgery, Affiliated Hospital of Qinghai University, Xining, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, General Electrics (GE) Healthcare, Beijing, China
| | - Yanjun Chai
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Zhiyong Zhao
- Second Clinical School, Lanzhou University, Lanzhou, China.,Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
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Niwa S, Ichikawa K, Kawashima H, Takata T, Minami S, Mitsui W. Reduction of streak artifacts caused by low photon counts utilizing an image-based forward projection in computed tomography. Comput Biol Med 2021; 135:104583. [PMID: 34216891 DOI: 10.1016/j.compbiomed.2021.104583] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/02/2021] [Accepted: 06/11/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND The streak artifacts in computed tomography (CT) images caused by low photon counts are known to be effectively suppressed by raw-data-based techniques. This study aims to propose a technique to reduce the streak artifact without accessing the raw data. METHODS The proposed streak artifact reduction (SAR) technique consists of three steps: numerical forward projection to a CT image, adaptive filtering of the generated sinogram, and image reconstruction from the processed sinogram. The authors have expanded the two-dimensional method (2D-SAR) to three dimensions (3D-SAR) by using consecutive CT images. The modulation transfer function (MTF), the image noise (standard deviation), and the visibility of comb-shaped objects were evaluated at a low dose of 5 mGy. Using anthropomorphic abdominal and chest phantoms, CT images and the artifact index (AI) were compared between 3D-SAR and two types of iterative reconstruction (IR). RESULTS Sufficient artifact reductions associated with 54% and 61% reduction of noise for 2D- and 3D-SAR, respectively, were obtained in the phantom images, although the 50%MTF decreased by 28%. The visibility of the combs was improved with both the 2D- and 3D-SAR methods. The AI results of 3D-SAR were better than one type of IR and almost equal to the other type of IR, which was consistent with observed artifacts. CONCLUSION Both 2D-SAR and 3D-SAR have turned out to be effective in reducing streak artifacts. The proposed technique will be an effective tool since it needs no raw data, and thus can be applied to any CT images produced by a wide variety of CT systems.
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Affiliation(s)
- Shinji Niwa
- Department of Medical Technology, Nakatsugawa Municipal General Hospital, 1522-1 Komanba, Nakatsugawa, Gifu, 508-0011, Japan; Division of Health Sciences, Graduate School of Medical Science, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
| | - Katsuhiro Ichikawa
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Hiroki Kawashima
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Tadanori Takata
- Radiology Division, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.
| | - Shuhei Minami
- Radiology Division, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.
| | - Wataru Mitsui
- Radiology Division, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.
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Cao Y, Zhang G, Zhang J, Yang Y, Ren J, Yan X, Wang Z, Zhao Z, Huang X, Bao H, Zhou J. Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study. Front Oncol 2021; 11:687771. [PMID: 34178682 PMCID: PMC8222982 DOI: 10.3389/fonc.2021.687771] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/17/2021] [Indexed: 12/29/2022] Open
Abstract
Background This study aimed to develop and validate a computed tomography (CT)-based radiomics model to predict microsatellite instability (MSI) status in colorectal cancer patients and to identify the radiomics signature with the most robust and high performance from one of the three phases of triphasic enhanced CT. Methods In total, 502 colorectal cancer patients with preoperative contrast-enhanced CT images and available MSI status (441 in the training cohort and 61 in the external validation cohort) were enrolled from two centers in our retrospective study. Radiomics features of the entire primary tumor were extracted from arterial-, delayed-, and venous-phase CT images. The least absolute shrinkage and selection operator method was used to retain the features closely associated with MSI status. Radiomics, clinical, and combined Clinical Radiomics models were built to predict MSI status. Model performance was evaluated by receiver operating characteristic curve analysis. Results Thirty-two radiomics features showed significant correlation with MSI status. Delayed-phase models showed superior predictive performance compared to arterial- or venous-phase models. Additionally, age, location, and carcinoembryonic antigen were considered useful predictors of MSI status. The Clinical Radiomics nomogram that incorporated both clinical risk factors and radiomics parameters showed excellent performance, with an AUC, accuracy, and sensitivity of 0.898, 0.837, and 0.821 in the training cohort and 0.964, 0.918, and 1.000 in the validation cohort, respectively. Conclusions The proposed CT-based radiomics signature has excellent performance in predicting MSI status and could potentially guide individualized therapy.
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Affiliation(s)
- Yuntai Cao
- Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.,Key Laboratory of Medical Imaging, Lanzhou, China
| | - Guojin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.,Sichuan Academy of Medical Sciences Sichuan Provincial People's Hospital, Chengdu, China
| | - Jing Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.,The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Yingjie Yang
- Department of Radiology, Second People's Hospital of Lanzhou City, Lanzhou, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China
| | - Xiaohong Yan
- Department of Critical Medicine, Affiliated Hospital of Qinghai University, Xining, China
| | - Zhan Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Zhiyong Zhao
- Second Clinical School, Lanzhou University, Lanzhou, China.,Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.,Key Laboratory of Medical Imaging, Lanzhou, China
| | - Xiaoyu Huang
- Second Clinical School, Lanzhou University, Lanzhou, China.,Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.,Key Laboratory of Medical Imaging, Lanzhou, China
| | - Haihua Bao
- Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.,Key Laboratory of Medical Imaging, Lanzhou, China
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Cao Y, Zhang G, Bao H, Zhang S, Zhang J, Zhao Z, Zhang W, Li W, Yan X, Zhou J. Development of a dual-energy spectral CT based nomogram for the preoperative discrimination of mutated and wild-type KRAS in patients with colorectal cancer. Clin Imaging 2020; 69:205-212. [PMID: 32920468 DOI: 10.1016/j.clinimag.2020.08.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/17/2020] [Accepted: 08/24/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE To develop a dual-energy spectral CT (DESCT) nomogram for the preoperative identification of KRAS mutation in patients with colorectal cancer (CRC). METHOD One hundred and twenty-four patients who underwent energy spectrum CT pre-operatively were recruited and split into mutated KRAS group (n = 50) and wild-type KRAS group (n = 74). DESCT parameters, including monochromatic CT value, iodine concentration, water concentration, and effective atomic number were measured independently by two reviewers in the arterial, venous, and delayed phases. Normalized iodine concentration (NIC) and slope k of the spectral HU curve were calculated. Evaluate other imaging features such as ATL/LTL ratio, tumor gross pattern, pericolorectal fat invasion (PFI) was also performed by these reviewers. Independent predictors for KRAS mutation were screened out using logistic regression, and these predictors were presented as a nomogram. The receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram. RESULTS The slope k in the arterial phase, effective atomic number in the arterial phase, NIC in the venous phase, ATL/LTL ratio and PFI were significant independent predictors for KRAS mutation. Based on these independent predictors, a quantitative nomogram was developed to predict individual KRAS mutation probability. The nomogram had excellent performance with an AUC of 0.848 and excellent calibration. DCA showed that our nomogram has outstanding clinical utility. CONCLUSIONS This study demonstrates that a DESCT based nomogram has potential value for individual preoperative identification of KRAS mutation in CRC patients.
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Affiliation(s)
- Yuntai Cao
- Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging, Gansu Province, Lanzhou, China; Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China; Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Guojin Zhang
- Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging, Gansu Province, Lanzhou, China
| | - Haihua Bao
- Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China
| | - Shenghui Zhang
- Department of Physics, University of Illinois at Chicago, Chicago, USA
| | - Jing Zhang
- Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging, Gansu Province, Lanzhou, China
| | - Zhiyong Zhao
- Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging, Gansu Province, Lanzhou, China
| | - Wenjuan Zhang
- Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging, Gansu Province, Lanzhou, China
| | - Weixia Li
- Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China
| | - Xiaohong Yan
- Department of Critical Medicine, Affiliated Hospital of Qinghai University, Xining, China
| | - Junlin Zhou
- Key Laboratory of Medical Imaging, Gansu Province, Lanzhou, China; Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
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Ichikawa K, Kawashima H, Shimada M, Adachi T, Takata T. A three-dimensional cross-directional bilateral filter for edge-preserving noise reduction of low-dose computed tomography images. Comput Biol Med 2019; 111:103353. [DOI: 10.1016/j.compbiomed.2019.103353] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/06/2019] [Accepted: 07/07/2019] [Indexed: 11/30/2022]
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