1
|
Xia F, Guo F, Liu Z, Zeng J, Ma X, Yu C, Li C. Enhanced CT combined with texture analysis for differential diagnosis of pleomorphic adenoma and adenolymphoma. BMC Med Imaging 2023; 23:169. [PMID: 37891554 PMCID: PMC10612226 DOI: 10.1186/s12880-023-01129-9] [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: 03/13/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
OBJECTIVE This study sought to evaluate the worth of the general characteristics of enhanced CT images and the histogram parameters of each stage in distinguishing pleomorphic adenoma (PA) and adenolymphoma (AL). METHODS The imaging features and histogram parameters of preoperative enhanced CT images in 20 patients with PA and 29 patients with AL were analyzed. Tumor morphology and histogram parameters of PA and AL were compared. Area under the curve (AUC), sensitivity, and subject operational feature specificity (ROC) analysis were used to determine the differential diagnostic effect of single-stage or multi-stage parameter combinations. RESULTS The difference in CT value and net enhancement value of arterial phase (AP) were significant (p < 0.05); Flat sweep phase (FSP), AP mean, percentiles, 10th, 50th, 90th, 99th and arterial period variance and venous phase (VP) kurtosis in the nine histogram parameters of each period (p < 0.05). An analysis of the ROC curve revealed a maximum area beneath the curve (AUC) in the 90th percentile of FSP for a single-parameter differential diagnosis to be 0.870. The diagnostic efficacy of the mean value of FSP + The 90th percentile of AP + Kurtosis of VP was the best in multi-parameter combination diagnosis, with an AUC of 0.925, and the sensitivity and specificity of 0.900 and 0.850, respectively. CONCLUSION The histogram analysis of enhanced CT images is valuable for the differentiation of PA and AL. Moreover, the combination of single-stage parameters or multi-stage parameters can improve the differential diagnosis efficiency.
Collapse
Affiliation(s)
- Feifei Xia
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Shihezi University, Shihezi, 832000, China
| | - Foqing Guo
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Shihezi University, Shihezi, 832000, China
| | - Zhe Liu
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Shihezi University, Shihezi, 832000, China
| | - Jie Zeng
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Shihezi University, Shihezi, 832000, China
| | - Xuehua Ma
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Shihezi University, Shihezi, 832000, China
| | - Chongqing Yu
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Shihezi University, Shihezi, 832000, China
| | - Changxue Li
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Shihezi University, Shihezi, 832000, China.
| |
Collapse
|
2
|
Tanişman Ö, Kiziltepe FT, Yildirim Ç, Coşar ZS. Prediction of prognostic factors in breast cancer: A noninvasive method utilizing histogram parameters derived from Adc maps. Heliyon 2023; 9:e16282. [PMID: 37251865 PMCID: PMC10208937 DOI: 10.1016/j.heliyon.2023.e16282] [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: 11/03/2022] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 05/31/2023] Open
Abstract
Objective The aim of this study is to investigate the relationship between histogram parameters and prognostic factors of breast cancer and to reveal the diagnostic performance of histogram parameters in predicting prognostic factors status. Materials and methods Ninety-two patients with a confirmed histopathological diagnosis of breast cancer were included in the study. Magnetic resonance imaging (MRI) was performed using a 1.5T scanner and two different b values were used for diffusion-weighted imaging (DWI) (b values: 0 s/mm2, b: 800 s/mm2). For 3D histogram analysis, regions of interest (ROI) were drawn each slice of the lesion on apparent diffusion coefficient (ADC) maps. The following data were derived from the histogram analysis data: percentiles, skewness, kurtosis, and entropy. The relationship between prognostic factors and histogram analysis data was investigated using the Kolmogorov-Smirnov test, Shapiro-Wilk test, skewness-kurtosis test, independent t-test, Mann-Whitney U test, and Kruskal-Wallis test. Receiver operator characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the histogram parameters. Results ADCmax, kurtosis, and entropy parameters were statistically significantly correlated with tumor diameter (p = 0.002, p = 0.008, and p = 0.001, respectively). There was a significant difference in ADC90% and ADCmax values, depending on estrogen receptor (ER) and progesterone receptor (PR) status. These values were lower in ER- and PR-positive than ER- and PR-negative patients (p = 0.02 and p = 0.001 vs. p = 0.018, p = 0.008). All ADC percentage values were lower in patients with a positive Ki-67 proliferation index as compared with those with a negative Ki-67 proliferation index (all p = 0.001). The entropy value was high in high-grade lesions and lesions with axillary involvement (p = 0.039 and p = 0.048, respectively). The highest area under the curve (AUC) for ER and PR status was calculated for the ADC90% value with ROC curve analysis. The highest AUC for Ki-67 proliferation index was found for the ADC50%. Conclusion Histogram analysis parameters derived from of ADC maps of whole lesions can reflect histopathological features of the tumors. Based on our study, it was concluded that histogram analysis parameters were related to the prognostic factors of the tumor.
Collapse
Affiliation(s)
- Özge Tanişman
- Deparment of Radiology, Oltu State Hospital, Erzurum, Turkey
| | - Fatma Tuba Kiziltepe
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Çiğdem Yildirim
- Department of Pathology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Zehra Sumru Coşar
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| |
Collapse
|
3
|
Feature generation and multi-sequence fusion based deep convolutional network for breast tumor diagnosis with missing MR sequences. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
|
4
|
Xiang L, Yang H, Qin Y, Wen Y, Liu X, Zeng WB. Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions. Front Oncol 2023; 12:1075072. [PMID: 36713551 PMCID: PMC9878824 DOI: 10.3389/fonc.2022.1075072] [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: 10/20/2022] [Accepted: 12/21/2022] [Indexed: 01/13/2023] Open
Abstract
Objective To investigate the diagnostic value of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) whole-lesion histogram parameters in differentiating benign and malignant solitary pulmonary lesions (SPLs). Materials and Methods Patients with SPLs detected by chest CT examination and with further routine MRI, DKI and IVIM-DWI functional sequence scanning data were recruited. According to the pathological results, SPLs were divided into a benign group and a malignant group. Independent samples t tests (normal distribution) or Mann‒Whitney U tests (nonnormal distribution) were used to compare the differences in DKI (Dk, K), IVIM (D, D*, f) and ADC whole-lesion histogram parameters between the benign and malignant SPL groups. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the histogram parameters and determine the optimal threshold. The area under the curve (AUC) of each histogram parameter was compared by the DeLong method. Spearman rank correlation was used to analyze the correlation between histogram parameters and malignant SPLs. Results Most of the histogram parameters for diffusion-related values (Dk, D, ADC) of malignant SPLs were significantly lower than those of benign SPLs, while most of the histogram parameters for the K value of malignant SPLs were significantly higher than those of benign SPLs. DKI (Dk, K), IVIM (D) and ADC were effective in differentiating benign and malignant SPLs and combined with multiple parameters of the whole-lesion histogram for the D value, had the highest diagnostic efficiency, with an AUC of 0.967, a sensitivity of 90.00% and a specificity of 94.03%. Most of the histogram parameters for the Dk, D and ADC values were negatively correlated with malignant SPLs, while most of the histogram parameters for the K value were positively correlated with malignant SPLs. Conclusions DKI (Dk, K) and IVIM (D) whole-lesion histogram parameters can noninvasively distinguish benign and malignant SPLs, and the diagnostic performance is better than that of DWI. Moreover, they can provide additional information on SPL microstructure, which has important significance for guiding clinical individualized precision diagnosis and treatment and has potential clinical application value.
Collapse
Affiliation(s)
- Lu Xiang
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, China,College of Medical Imaging, North Sichuan Medical College, Sichuan, China
| | - Hong Yang
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, China,Chongqing University School of Medicine, Chongqing, China
| | - Yu Qin
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, China,College of Medical Imaging, North Sichuan Medical College, Sichuan, China
| | - Yun Wen
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, China
| | - Xue Liu
- PET-CT Center, Chongqing University Three Gorges Hospital, Chongqing, China,*Correspondence: Xue Liu, ; Wen-Bing Zeng,
| | - Wen-Bing Zeng
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, China,*Correspondence: Xue Liu, ; Wen-Bing Zeng,
| |
Collapse
|
5
|
AĞLAMIŞ S, BAYKARA M. Meme manyetik rezonans görüntülemede malign ve benign lezyonların ayrımında histogram analizi: ön çalışma. CUKUROVA MEDICAL JOURNAL 2022. [DOI: 10.17826/cumj.1090183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Purpose: The present study assesses whether malignant and benign lesions can be distinguished through histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted breast magnetic resonance images (MRIs).
Materials and Methods: MRIs of 20 malignant and 20 benign breast lesions were reviewed retrospectively by histogram analysis performed using Osirix V.4.9 software. The regions of interest (ROIs) were drawn manually to include almost the entire lesion, and values from these ROIs were used to calculate gray-level intensity mean, standard deviation, entropy, uniformity, skewness, kurtosis, and percentile values.
Results: In non-fat-suppressed T1-weighted images, the minimum, 1st, 3rd, 5th, 10th and 25th percentile values were significantly lower in the malignant lesions than in the benign lesions. The minimum value had sensitivity of 70% and specificity of 63.2%. On the fat-suppressed T2-weighted images, skewness was significantly higher while uniformity was significantly lower in malignant lesions than benign lesions. Skewness had 68.4% sensitivity and 60% specificity, and uniformity had 65% sensitivity and 68.4% specificity.
Conclusion: The results of this study demonstrated that histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted images can be used to differentiate malignant and benign lesions in breast MRI.
Collapse
|
6
|
Ao F, Yan Y, Zhang ZL, Li S, Li WJ, Chen GB. The value of dynamic contrast-enhanced magnetic resonance imaging combined with apparent diffusion coefficient in the differentiation of benign and malignant diseases of the breast. Acta Radiol 2022; 63:891-900. [PMID: 34134527 DOI: 10.1177/02841851211024002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The value of combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) histogram analysis for the diagnosis of breast cancer has not been evaluated in previous studies. PURPOSE To investigate the diagnostic value of DCE-MRI combined with ADC in benign and malignant breast lesions. MATERIAL AND METHODS The clinicopathological imaging data included 168 patients (177 lesions) with breast lesions who underwent convention breast MRI, DCE-MRI, and diffusion-weighted imaging (DWI); they were divided into the benign lesion group (n = 39) and malignant lesion group (n = 129) based on pathology. RESULTS Using the type III outflow curve as a diagnostic criterion for malignant breast lesions, the diagnostic sensitivity was 76.9%, the specificity was 80%, the correct rate was 72.2%, and its area under the curve (AUC) was 0.823. Using an enhancement ratio > 100% as a diagnostic criterion for malignant breast lesions, the sensitivity was 61.5%, specificity was 80%, and AUC was 0.723. Using > 3 ipsilateral vessels as a diagnostic criterion for malignant lesions in the breast resulted in a diagnostic sensitivity of 81.6%, a specificity of 80.8%, and an AUC of 0.805. CONCLUSION The type of time intensity curve DCE-MRI, the early enhancement rate in the first phase, the number of ipsilateral vessels, and the ADC full volume histogram of the blood supply score and DWI are valuable in the diagnosis of benign and malignant breast lesions.
Collapse
Affiliation(s)
- Feng Ao
- Department of Medical Imaging Center, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, PR China
| | - Yi Yan
- Institute of Ophthalmology Center, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, PR China
| | - Zi-Li Zhang
- Department of Medical Imaging Center, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, PR China
| | - Sheng Li
- Department of Medical Imaging Center, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, PR China
| | - Wen-Jing Li
- Department of Medical Imaging Center, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, PR China
| | - Guang-Bin Chen
- Department of Medical Imaging Center, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, PR China
| |
Collapse
|
7
|
Zackrisson S, Andersson I. The development of breast radiology: the Acta Radiologica perspective. Acta Radiol 2021; 62:1473-1480. [PMID: 34709078 DOI: 10.1177/02841851211050861] [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: 12/26/2022]
Abstract
The encouraging results of modern breast cancer care builds on tremendous improvements in diagnostics and therapy during the 20th century. Scandinavian countries have made important footprints in the development of breast diagnostics regarding technical development of imaging, cell and tissue sampling methods and, not least, population screening with mammography. The multimodality approach in combination with multidisciplinary clinical work in breast cancer serve as a role model for the management of many cancer types worldwide. The development of breast radiology is well represented in the research published in this journal and this historical review will describe the most important steps.
Collapse
Affiliation(s)
- Sophia Zackrisson
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - Ingvar Andersson
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Unilabs Breast Center, Skåne University Hospital Malmö, Malmö, Sweden
| |
Collapse
|
8
|
Lee YJ, Kim SH, Kang BJ, Son YH, Grimm R. Associations between angiogenic factors and intravoxel incoherent motion-derived parameters in diffusion-weighted magnetic resonance imaging of breast cancer. Medicine (Baltimore) 2021; 100:e27495. [PMID: 34731130 PMCID: PMC8519258 DOI: 10.1097/md.0000000000027495] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 09/23/2021] [Indexed: 01/05/2023] Open
Abstract
Intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) can be used to estimate perfusion-related parameters, but these parameters may differ, based on the curve-fitting algorithm used for IVIM. Microvessel density (MVD) and vascular endothelial growth factor (VEGF) status are used as angiogenic factors in breast cancer. We aimed to investigate the relationship between MVD, VEGF, and intravoxel incoherent motion (IVIM)-derived parameters, obtained by 4 curve-fitting algorithms, in patients with invasive breast cancers.This retrospective study investigated IVIM-derived parameters, D (ie, tissue diffusivity), D∗ (ie, pseudodiffusivity), and f (ie, perfusion fraction), of 55 breast cancers, using 10 b values (range, 0-800 s/mm2) and 4 curve-fitting algorithms: algorithm 1, linear fitting of D and f first, followed by D∗; algorithm 2, linear fitting of D and f and nonlinear fitting of D∗; algorithm 3, linear fitting of D and f, linear fitting of D∗, and ignoring D contribution for low b values; and algorithm 4, full nonlinear fitting of D, f, and D∗. We evaluated whole-tumor histograms of D, f, and D∗ for their association with MVD and VEGF.D∗10, D∗25, D∗50, D∗mean, D∗75, D∗90, f10, and f25, derived using algorithm 3, were associated with VEGF expression (P = .043, P = 0.012, P = .019, P = .024, P = .044, P = .041, P = .010, and P = .005, respectively). However, no correlation existed between MVD and IVIM-derived parameters.Perfusion-related IVIM parameters obtained by curve-fitting algorithm 3 may reflect VEGF expression.
Collapse
Affiliation(s)
- Youn Joo Lee
- Department of Radiology, Daejeon St. Mary's Hospital, Daejeon
| | - Sung Hun Kim
- Seoul St. Mary's Hospital, The Catholic University of Korea, Republic of Korea
| | - Bong Joo Kang
- Seoul St. Mary's Hospital, The Catholic University of Korea, Republic of Korea
| | | | | |
Collapse
|
9
|
Arita Y, Yoshida S, Kwee TC, Akita H, Okuda S, Iwaita Y, Mukai K, Matsumoto S, Ueda R, Ishii R, Mizuno R, Fujii Y, Oya M, Jinzaki M. Diagnostic value of texture analysis of apparent diffusion coefficient maps for differentiating fat-poor angiomyolipoma from non-clear-cell renal cell carcinoma. Eur J Radiol 2021; 143:109895. [PMID: 34388418 DOI: 10.1016/j.ejrad.2021.109895] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/15/2021] [Accepted: 08/02/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE To investigate the feasibility of texture analysis of apparent diffusion coefficient (ADC) maps for differentiating fat-poor angiomyolipomas (fpAMLs) from non-clear-cell renal cell carcinomas (non-ccRCCs). METHODS In this bi-institutional study, we included two consecutive cohorts from different institutions with pathologically confirmed solid renal masses: 67 patients (fpAML = 46; non-ccRCC = 21) for model development and 39 (fpAML = 24; non-ccRCC = 15) for validation. Patients underwent preoperative magnetic resonance imaging (MRI), including diffusion-weighted imaging. We extracted 45 texture features using a software with volumes of interest on ADC maps. Receiver operating characteristic curve analysis was performed to compare the diagnostic performance between the random forest (RF) model (derived from extracted texture features) and conventional subjective evaluation using computed tomography and MRI by radiologists. RESULTS RF analysis revealed that grey-level zone length matrix long-zone high grey-level emphasis was the dominant texture feature for diagnosing fpAML. The area under the curve (AUC) of the RF model to distinguish fpAMLs from non-ccRCCs was not significantly different between the validation and development cohorts (p = .19). In the validation cohort, the AUC of the RF model was similar to that of board-certified radiologists (p = .46) and significantly higher than that of radiology residents (p = .03). CONCLUSIONS Texture analysis of ADC maps demonstrated similar diagnostic performance to that of board-certified radiologists for discriminating between fpAMLs and non-ccRCCs. Diagnostic performances in the development and validation cohorts were comparable despite using data from different imaging device manufacturers and institutions.
Collapse
Affiliation(s)
- Yuki Arita
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.
| | - Soichiro Yoshida
- Department of Urology, Tokyo Medical and Dental University Graduate School, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan.
| | - Thomas C Kwee
- Department of Radiology, Nuclear Medicine, and Molecular Imaging, University Medical Center Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB Groningen, the Netherlands
| | - Hirotaka Akita
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.
| | - Shigeo Okuda
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.
| | - Yuki Iwaita
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.
| | - Kiyoko Mukai
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.
| | - Shunya Matsumoto
- Department of Urology, Tokyo Medical and Dental University Graduate School, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Ryo Ueda
- Office of Radiation Technology, Keio University Hospital, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan
| | - Ryota Ishii
- Biostatistics Unit, Clinical and Translational Research Center, Keio University Hospital, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.
| | - Ryuichi Mizuno
- Department of Urology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.
| | - Yasuhisa Fujii
- Department of Urology, Tokyo Medical and Dental University Graduate School, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan.
| | - Mototsugu Oya
- Department of Urology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.
| |
Collapse
|