Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI

Jin, Jin; Zhang, Lin; Leng, Ethan; Metzger, Gregory J.; Koopmeiners, Joseph S.

doi:10.1080/02664763.2021.2017411

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…Therefore, we settled on a 3-fold increase in resolution, to balance the need to improve resolution with need to maintain a clinically acceptable scan time. In future studies, our technology may be integrated with other technologies in development such as high-resolution DCE ( 29, 30 ), quantitative T1 and T2 mapping ( 31 ), super-resolution reconstruction ( 32 ), and artificial intelligence–driven image analysis ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance

Saouaf,

Xie,

Kim

et al. 2024

Cancer Research Communications

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Purpose: Majority of men with low-risk prostate cancer can be managed with active surveillance (AS). This study evaluates a high-resolution diffusion-weighted imaging (HR-DWI) technique to predict adverse biopsy histology (AH), defined as Gleason score >=7 on any biopsy or >=3 increase in number of positive biopsy cores on systematic biopsies. We test the hypothesis that high grade disease and progressing disease undergo subtle changes during even short intervals that can be detected by HR-DWI. Patients and Methods: In a prospective clinical trial, serial multiparametric MRIs, incorporating HR-DWI and standard DWI (S-DWI) were performed approximately 12 months apart prior to prostate biopsy (n=59). HR-DWI, which uses reduced field-of-view and motion compensation techniques, was compared to S-DWI. Results: HR-DWI had a 3-fold improvement in special resolution compared with S-DWI as confirmed using imaging phantoms. For detecting AH, multi-parametric MRI using HR-DWI had a sensitivity of 75% and specificity of 83.9%, and MRI using S-DWI had a sensitivity of 71.4% and specificity of 54.8%. The area under the curve for HR-DWI was significantly higher (0.794 vs 0.631, p=0.014). Secondary analyses of univariable predictors of AH showed tumor size increase (OR 16.8, 95% CI 4.06-69.48, p<0.001) and apparent diffusion coefficient (ADC) decrease (OR 5.06, 95% CI 1.39-18.38, p=0.014) on HR-DWI were significant predictors of AH. Conclusion: HR-DWI outperforms S-DWI in predicting AH. Patient with AH have tumors that change in size and ADC that could be detected using HR-DWI. Future studies with longer follow-up should assess HR-DWI for predicting disease progression during AS.

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