Dual-energy computed tomography with new virtual monoenergetic image reconstruction enhances prostate lesion image quality and improves the diagnostic efficacy for prostate cancer

被引:1
作者
Fan, Nina [1 ]
Chen, Xiaofeng [1 ]
Li, Yulin [1 ]
Zhu, Zhiqiang [1 ]
Chen, Xiangguang [1 ]
Yang, Zhiqi [1 ]
Yang, Jiada [1 ]
机构
[1] Meizhou Peoples Hosp, Dept Radiol, Meizhou 514000, Guangdong, Peoples R China
关键词
Prostate cancer; Dual-energy computed tomography; Virtual monoenergetic images in arterial phase; Diagnostic performance; CT;
D O I
10.1186/s12880-024-01393-3
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background Prostate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer. Methods We conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People's Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40-100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images. Results Virtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 +/- 57.14) compared to conventional linear blending images (66.66 +/- 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 +/- 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 +/- 2.13) and 50 keV (2.95 +/- 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849). Conclusions Virtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.
引用
收藏
页数:10
相关论文
共 41 条
[1]   Risk Assessment of Computer-Aided Diagnostic Software for Hepatic Resection [J].
Akhtar, Yusuf ;
Dakua, Sarada Prasad ;
Abdalla, Alhusain ;
Aboumarzouk, Omar Mousa ;
Ansari, Mohammed Yusuf ;
Abinahed, Julien ;
Elakkad, Mohamed Soliman Mohamed ;
Al-Ansari, Abdulla .
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES, 2022, 6 (06) :667-677
[2]   Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review [J].
Ansari, Mohammed Yusuf ;
Mangalote, Iffa Afsa Changaai ;
Meher, Pramod Kumar ;
Aboumarzouk, Omar ;
Al-Ansari, Abdulla ;
Halabi, Osama ;
Dakua, Sarada Prasad .
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2024, 8 (03) :2126-2149
[3]   Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade [J].
Ansari, Mohammed Yusuf ;
Qaraqe, Marwa ;
Charafeddine, Fatme ;
Serpedin, Erchin ;
Righetti, Raffaella ;
Qaraqe, Khalid .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2023, 146
[4]   Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing [J].
Ansari, Mohammed Yusuf ;
Chandrasekar, Vaisali ;
Singh, Ajay Vikram ;
Dakua, Sarada Prasad .
IEEE ACCESS, 2023, 11 :9890-9906
[5]   MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East [J].
Ansari, Mohammed Yusuf ;
Qaraqe, Marwa .
IEEE ACCESS, 2023, 11 :4589-4601
[6]   Dense-PSP-UNet: A neural network for fast inference liver ultrasound segmentation [J].
Ansari, Mohammed Yusuf ;
Yang, Yin ;
Meher, Pramod Kumar ;
Dakua, Sarada Prasad .
COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 153
[7]   Practical utility of liver segmentation methods in clinical surgeries and interventions [J].
Ansari, Mohammed Yusuf ;
Abdalla, Alhusain ;
Ansari, Mohammed Yaqoob ;
Ansari, Mohammed Ishaq ;
Malluhi, Byanne ;
Mohanty, Snigdha ;
Mishra, Subhashree ;
Singh, Sudhansu Sekhar ;
Abinahed, Julien ;
Al-Ansari, Abdulla ;
Balakrishnan, Shidin ;
Dakua, Sarada Prasad .
BMC MEDICAL IMAGING, 2022, 22 (01)
[8]  
Ansari M, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-16828-6
[9]   Virtual monoenergetic dual-layer dual-energy CT images in colorectal cancer: CT diagnosis could be improved? [J].
Arico, Francesco Marcello ;
Trimarchi, Renato ;
Portaluri, Antonio ;
Barilla, Claudia ;
Migliaccio, Nicola ;
Bucolo, Giuseppe Mauro ;
Cicero, Giuseppe ;
Sofia, Carmelo ;
Booz, Christian ;
Vogl, Thomas J. J. ;
Marino, Maria Adele ;
Ascenti, Velio ;
D'Angelo, Tommaso ;
Mazziotti, Silvio ;
Ascenti, Giorgio .
RADIOLOGIA MEDICA, 2023, 128 (08) :891-899
[10]   Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta [J].
Chandrasekar, Vaisali ;
Ansari, Mohammed Yusuf ;
Singh, Ajay Vikram ;
Uddin, Shahab ;
Prabhu, Kirthi S. ;
Dash, Sagnika ;
Khodor, Souhaila Al ;
Terranegra, Annalisa ;
Avella, Matteo ;
Dakua, Sarada Prasad .
IEEE ACCESS, 2023, 11 :52726-52739