Publications by authors named "Katie M Merriman"
Objective: To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.
Materials And Methods: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system.
Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results.
View Article and Find Full Text PDFRationale And Objectives: Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI.
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- This study assesses the effectiveness of three AI algorithms for segmenting prostate regions in MRI scans of patients with complex medical backgrounds and varied anatomical features.
- The researchers analyzed data from 683 MRI scans, ensuring that they included criteria such as previous treatments and different scanner qualities, and compared the AI’s segmentation against expert radiologist assessments.
- Results showed that deep learning models significantly outperformed other methods, especially in cases with smaller prostate volumes and better image quality, highlighting the challenges presented by variances in anatomy and scan conditions.
Rationale And Objectives: Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI.
View Article and Find Full Text PDFBackground: Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.
Purpose: To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm.
Study Type: Retrospective.
Article Synopsis
- MRI/ultrasound fusion-guided targeted biopsy (TBx) is essential for effective management of prostate cancer by helping with precise risk stratification.
- This study compared the performance of two versions of the PI-RADS scoring system (v2.0 and v2.1) in predicting changes in cancer severity after TBx and subsequent radical prostatectomy (RP).
- Results showed no significant differences in upgrade or downgrade rates between the two versions, indicating that both versions are equivalent in assessing clinically significant prostate cancer.
Article Synopsis
- The study aimed to assess a biparametric MRI-based AI model for detecting local prostate cancer recurrence in post-radiotherapy patients.
- Of 62 patients analyzed, the AI model identified 40 lesions, but its detection performance was lower than that of radiologists, with sensitivity rates of 76.1% compared to 91.3%.
- The AI showed better results in patients treated with external beam radiation therapy, but further validation is needed before it can be used clinically.
Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018.
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