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Role of Artificial Intelligence in MRI-Based Rectal Cancer Staging: A Systematic Review.
Cureus
December 2024
Internal Medicine, Belgaum Institute of Medical Science, Belgaum, IND.
Several studies explored the application of artificial intelligence (AI) in magnetic resonance imaging (MRI)-based rectal cancer (RC) staging, but a comprehensive evaluation remains lacking. This systematic review aims to review the performance of AI models in MRI-based RC staging. PubMed and Embase were searched from the inception of the database till October 2024 without any language and year restrictions.
View Article and Find Full Text PDFHigh performance of the diffusion magnetic resonance imaging biomarker diffusion-derived 'vessel density' (DDVD) for separating placentas associated with pre-eclampsia from placentas in normal pregnancy.
Quant Imaging Med Surg
January 2025
Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
Background: Diffusion-derived 'vessel density' (DDVD) is a surrogate of the area of micro-vessels per unit tissue. DDVD is calculated according to: DDVD (b0b50) = Sb0/ROIarea0 - Sb50/ROIarea50, where Sb0 and Sb50 refer to the tissue signal when is 0 or 50 s/mm. Due to the complexity of pre-eclampsia (PE), even a combination of risk factors and available tests cannot accurately diagnose or predict PE.
View Article and Find Full Text PDFDevelopment and validation of a multi-parametric MRI deep-learning model for preoperative lymphovascular invasion evaluation in rectal cancer.
Quant Imaging Med Surg
January 2025
Department of Magnetic Resonance Imaging Diagnostic, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Background: Lymphovascular invasion (LVI) is an independent prognostic factor for patients with rectal cancer (RC). Recent studies have shown that deep learning (DL)-based magnetic resonance imaging (MRI) has potential in evaluating the treatment response of RC patients, but the role of MRI-based DL in assessing RC LVI remains unclear. This study sought to develop and validate a DL model to evaluate the LVI status of RC patients preoperatively based on MRI, and to test its performance at an external center.
View Article and Find Full Text PDFDifferentiation of early-stage endometrial carcinoma from benign endometrial lesions: a comparative study of six diffusion models.
Quant Imaging Med Surg
January 2025
Department of MRI, the First People's Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Background: Accurate differentiation between benign and malignant endometrial lesions holds substantial clinical importance. This study aimed to evaluate the efficacy of various diffusion models in the preoperative diagnosis of early-stage endometrial carcinoma (EC).
Methods: A total of 72 consecutive patients with benign or malignant endometrial lesions from the First People's Hospital of Yunnan Province were prospectively enrolled between April 2021 and July 2023.
Influences of the second motion probing gradient -value and T2 relaxation time on magnetic resonance diffusion-derived 'vessel density' (DDVD) calculation: the examples of liver, spleen, and liver simple cyst.
Quant Imaging Med Surg
January 2025
Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.
Background: Magnetic resonance (MR) diffusion-derived 'vessel density' (DDVD) is calculated according to: DDVD = Sb0/ROI - S/ROI, where S and S refer to the tissue signal when -value is 0 or 2 s/mm. S and ROI can also be approximated by other low -values diffusion-weighted imaging (DWI). This study investigates the influence of the second motion probing gradient -value and T2 on DDVD calculations of the liver, spleen, and liver simple cyst.
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