Objective: We aimed to explore the role of diffusion-weighted imaging (DWI) in combination with T2-weighted imaging (T2WI) in detecting prostate carcinoma through a systematic review and meta-analysis.
Materials And Methods: The MEDLINE, EMBASE, Cancerlit, and Cochrane Library databases were searched for studies published from January 2001 to July 2011 evaluating the diagnostic performance of T2WI combined with DWI in detecting prostate carcinoma. We determined sensitivities and specificities across studies, calculated positive and negative likelihood ratios, and constructed summary receiver operating characteristic curves. We also compared the performance of T2WI combined with DWI with T2WI alone by analyzing studies that had also used these diagnostic methods on the same patients.
Results: Across 10 studies (627 patients), the pooled sensitivity of T2WI combined with DWI was 0.76 (95% CI, 0.65-0.84), and the pooled specificity was 0.82 (95% CI, 0.77-0.87). Overall, the positive likelihood ratio was 4.31 (95% CI, 3.12-5.92), and the negative likelihood ratio was 0.29 (95% CI, 0.20-0.43). In seven studies in which T2WI combined with DWI and T2WI alone were performed, the sensitivity and specificity of T2WI combined with DWI were 0.72 (95% CI, 0.67-0.82) and 0.81 (95% CI, 0.76-0.86), respectively, and the sensitivity and specificity of T2WI alone were 0.62 (95% CI, 0.55-0.68) and 0.77 (95% CI, 0.71-0.82), respectively.
Conclusion: T2WI combined with DWI may be a valuable tool for detecting prostate cancer in the overall evaluation of prostate cancer, compared with T2WI alone. High-quality prospective studies of T2WI combined with DWI to detect prostate carcinoma still need to be conducted.
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http://dx.doi.org/10.2214/AJR.11.7634 | DOI Listing |
Abdom Radiol (NY)
January 2025
Department of Radiology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China.
Background: To develop and validate a clinical-radiomics model for preoperative prediction of lymphovascular invasion (LVI) in rectal cancer.
Methods: This retrospective study included data from 239 patients with pathologically confirmed rectal adenocarcinoma from two centers, all of whom underwent MRI examinations. Cases from the first center (n = 189) were randomly divided into a training set and an internal validation set at a 7:3 ratio, while cases from the second center (n = 50) constituted the external validation set.
Clin Radiol
December 2024
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China. Electronic address:
Arch Gynecol Obstet
January 2025
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi, China.
Purpose: This case report aims to present a rare case of endometrial carcinosarcoma, a highly malignant tumor with a poor prognosis. The primary objective is to describe this unique case's clinical presentation, multimodal magnetic resonance imaging (MRI) features, typical histopathological characteristics and surgical treatment.
Methods: A detailed analysis of the patient's medical history, preoperative imaging evaluation, and treatment approach was conducted.
Abdom Radiol (NY)
January 2025
Department of Radiology, Peking University People's Hospital, Beijing, China.
Purpose: Correctly classifying uterine fibroids is essential for treatment planning. The objective of this study was to assess the accuracy and reliability of the FIGO classification system in categorizing uterine fibroids via organ-axial T2WI and to further investigate the factors associated with uterine compression.
Methods: A total of 130 patients with ultrasound-confirmed fibroids were prospectively enrolled between March 2023 and May 2024.
BMC Med Imaging
January 2025
Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Purpose: We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs).
Methods: 279 features were extracted from each ROI including 9 histogram features, 220 Gy-level co-occurrence matrix features, 20 Gy-level run-length matrix features, 5 auto-regressive model features, 20 wavelets transform features and 5 absolute gradient statistics features. The datasets were randomly divided into two groups, the training set (~ 70%) and the test set (~ 30%).
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