Literature DB >> 32306078

Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy.

Marc R Liechti1, Urs J Muehlematter1, Aurelia F Schneider1, Daniel Eberli2, Niels J Rupp3, Andreas M Hötker1, Olivio F Donati1, Anton S Becker4,5.   

Abstract

OBJECTIVES: To assess interreader agreement of manual prostate cancer lesion segmentation on multiparametric MR images (mpMRI). The secondary aim was to compare tumor volume estimates between MRI segmentation and transperineal template saturation core needle biopsy (TTSB).
METHODS: We retrospectively reviewed patients who had undergone mpMRI of the prostate at our institution and who had received TTSB within 190 days of the examination. Seventy-eight cancer lesions with Gleason score of at least 3 + 4 = 7 were manually segmented in T2-weighted images by 3 radiologists and 1 medical student. Twenty lesions were also segmented in apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) series. First, 20 volumetric similarity scores were computed to quantify interreader agreement. Second, manually segmented cancer lesion volumes were compared with TTSB-derived estimates by Bland-Altman analysis and Wilcoxon testing.
RESULTS: Interreader agreement across all readers was only moderate with mean T2 Dice score of 0.57 (95%CI 0.39-0.70), volumetric similarity coefficient of 0.74 (0.48-0.89), and Hausdorff distance of 5.23 mm (3.17-9.32 mm). Discrepancy of volume estimate between MRI and TTSB was increasing with tumor size. Discrepancy was significantly different between tumors with a Gleason score 3 + 4 vs. higher grade tumors (0.66 ml vs. 0.78 ml; p = 0.007). There were no significant differences between T2, ADC, and DCE segmentations.
CONCLUSIONS: We found at best moderate interreader agreement of manual prostate cancer segmentation in mpMRI. Additionally, our study suggests a systematic discrepancy between the tumor volume estimate by MRI segmentation and TTSB core length, especially for large and high-grade tumors. KEY POINTS: • Manual prostate cancer segmentation in mpMRI shows moderate interreader agreement. • There are no significant differences between T2, ADC, and DCE segmentation agreements. • There is a systematic difference between volume estimates derived from biopsy and MRI.

Entities:  

Keywords:  Biopsy; Magnetic resonance imaging; Observer variation; Prostate cancer

Mesh:

Year:  2020        PMID: 32306078     DOI: 10.1007/s00330-020-06786-w

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  6 in total

1.  Evaluation of Traumatic Subdural Hematoma Volume by Using Image Segmentation Assessment Based on Deep Learning.

Authors:  Dan Chen; Lin Bian; Hao-Yuan He; Ya-Dong Li; Chao Ma; Lian-Gang Mao
Journal:  Comput Math Methods Med       Date:  2022-06-28       Impact factor: 2.809

2.  Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer.

Authors:  Haoxin Zheng; Qi Miao; Yongkai Liu; Sohrab Afshari Mirak; Melina Hosseiny; Fabien Scalzo; Steven S Raman; Kyunghyun Sung
Journal:  Eur Radiol       Date:  2022-03-03       Impact factor: 7.034

3.  Quantifying Tumor and Vasculature Deformations during Laryngoscopy.

Authors:  Xiaotian Wu; David A Pastel; Rihan Khan; Clifford J Eskey; Yuan Shi; Michael Sramek; Joseph A Paydarfar; Ryan J Halter
Journal:  Ann Biomed Eng       Date:  2022-01-07       Impact factor: 4.219

4.  Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer.

Authors:  Erlend Hodneland; Satheshkumar Kaliyugarasan; Kari Strøno Wagner-Larsen; Njål Lura; Erling Andersen; Hauke Bartsch; Noeska Smit; Mari Kyllesø Halle; Camilla Krakstad; Alexander Selvikvåg Lundervold; Ingfrid Salvesen Haldorsen
Journal:  Cancers (Basel)       Date:  2022-05-11       Impact factor: 6.575

5.  Prostate cancer measurements on serial MRI during active surveillance: it's time to be PRECISE.

Authors:  Francesco Giganti; Vasilis Stavrinides; Armando Stabile; Elizabeth Osinibi; Clement Orczyk; Jan Philipp Radtke; Alex Freeman; Aiman Haider; Shonit Punwani; Clare Allen; Mark Emberton; Alex Kirkham; Caroline M Moore
Journal:  Br J Radiol       Date:  2020-09-21       Impact factor: 3.039

6.  On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking.

Authors:  Orhun Utku Aydin; Abdel Aziz Taha; Adam Hilbert; Ahmed A Khalil; Ivana Galinovic; Jochen B Fiebach; Dietmar Frey; Vince Istvan Madai
Journal:  Eur Radiol Exp       Date:  2021-01-21
  6 in total

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