Intravoxel incoherent motion predicts positive surgical margins and Gleason score upgrading after radical prostatectomy for prostate cancer

Meng, Shuang; Gan, Wanting; Chen, Lihua; Wang, Nan; Liu, Ailian

doi:10.1007/s11547-023-01645-2

Cited by 2 publications

(2 citation statements)

References 48 publications

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“…Unfortunately, the two studies conducted by Meng et al [ 32 ] and Hu et al [ 33 ] in 2023, while being relevant to lesion segmentation, chose to employ manual segmentation methodologies rather than exploring automatic segmentation approaches. As a result, these studies do not contribute to the evaluation of automatic segmentation techniques as the focus of their investigation.…”

Section: Methodsmentioning

confidence: 99%

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Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI

Balaha,

Ayyad,

Alksas

et al. 2024

Bioengineering

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Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.

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Section: Methodsmentioning

confidence: 99%

“…Meng et al [ 32 ] aimed to evaluate the predictive utility of IVIM for positive surgical margins (PSMs) and Gleason score (GS) upgrading in PCa patients undergoing radical prostatectomy (RP). In a retrospective analysis of 106 eligible PCa patients, pelvic multiparametric Magnetic Resonance Imaging (mpMRI) was employed.…”

Section: Related Studiesmentioning

confidence: 99%