Literature DB >> 31175330

Histological differences in cancer cells, stroma, and luminal spaces strongly correlate with in vivo MRI-detectability of prostate cancer.

Kosuke Miyai1,2, Ayako Mikoshi3, Fumiko Hamabe3, Kuniaki Nakanishi4, Keiichi Ito5, Hitoshi Tsuda6, Hiroshi Shinmoto3.   

Abstract

The current study aimed to investigate the plausible histopathological factors that affect the detectability of prostate cancers on multiparametric magnetic resonance imaging (MP-MRI). This retrospective study included 59 consecutive patients who had undergone MP-MRI and subsequent radical prostatectomy. The cases were standardized according to the tumor size ranging from 10 to 20 mm on the final pathological diagnosis. Histopathological review and semi-automated imaging analysis were performed to evaluate the relative area fractions of the histological components, including cancer cells, stroma, and luminal spaces. Among the 59 prostatectomy specimens, no case showed two or more foci of cancer that matched the size criteria. Of the 59 lesions, 35 were MRI-detectable [Prostate Imaging Reporting and Data System (PIRADS) score of 3 or greater] and 24 were MRI-undetectable (PIRADS score of 2 or less). No significant differences were observed in Gleason Grade Group, percentage of Gleason pattern 4, and predominant subtype of Gleason pattern 4 between MRI-detectable and MRI-undetectable cancers. On the other hand, significantly higher mean area fraction of cancer cells (60.9% vs. 42.7%, P < 0.0001) and lower mean area fractions of stroma (33.8% vs. 45.1%, P = 0.00089) and luminal spaces (5.2% vs. 12.2%, P < 0.0001) were observed in MRI-detectable cancers than in MRI-undetectable cancers. In a multivariable analysis performed upon exclusion of area fraction of stroma due to its multicollinearity with that of cancer cells, area fractions of cancer cells (P = 0.0031) and luminal space (P = 0.0035) demonstrated strong positive and negative correlation with MRI-detectability, respectively. Changes in cancer cells, stroma, and luminal spaces, rather than conventional histological parameters, could be considered one of the best predictors to clinical, in vivo MRI-detectability of prostate cancer.

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Year:  2019        PMID: 31175330     DOI: 10.1038/s41379-019-0292-y

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  7 in total

1.  Evaluating the performance of clinical and radiological data in predicting prostate cancer in prostate imaging reporting and data system version 2.1 category 3 lesions of the peripheral and the transition zones.

Authors:  Caterina Gaudiano; Lorenzo Bianchi; Beniamino Corcioni; Francesca Giunchi; Riccardo Schiavina; Federica Ciccarese; Lorenzo Braccischi; Arianna Rustici; Michelangelo Fiorentino; Eugenio Brunocilla; Rita Golfieri
Journal:  Int Urol Nephrol       Date:  2021-11-25       Impact factor: 2.370

2.  Magnetic resonance imaging findings of pure prostatic ductal adenocarcinomas: a case series.

Authors:  Hiromi Edo; Yasuyo Urase; Yoshiko Ueno; Ayumu Kido; Tsutomu Tamada; Yudai Asano; Kentaro Ida; Hisataka Ito; Takashi Koyama; Kosuke Miyai; Hitoshi Tsuda; Hiroshi Shinmoto
Journal:  Abdom Radiol (NY)       Date:  2022-02-28

3.  Assessment of factors associated with PSA level in prostate cancer cases and controls from three geographical regions.

Authors:  Nishi Karunasinghe; Tsion Zewdu Minas; Bo-Ying Bao; Arier Lee; Alice Wang; Shuotun Zhu; Jonathan Masters; Megan Goudie; Shu-Pin Huang; Frank J Jenkins; Lynnette R Ferguson
Journal:  Sci Rep       Date:  2022-01-07       Impact factor: 4.379

4.  Intraepithelial lymphocytes are indicators of better prognosis in surgically resected endometrioid-type endometrial carcinomas at early and advanced stages.

Authors:  Takako Kono-Sato; Kosuke Miyai; Yoji Yamagishi; Morikazu Miyamoto; Masashi Takano; Susumu Matsukuma; Kimiya Sato; Hitoshi Tsuda
Journal:  BMC Cancer       Date:  2022-04-02       Impact factor: 4.430

5.  What Type of Prostate Cancer Is Systematically Overlooked by Multiparametric Magnetic Resonance Imaging? An Analysis from the PROMIS Cohort.

Authors:  Joseph M Norris; Lina M Carmona Echeverria; Simon R J Bott; Louise C Brown; Nick Burns-Cox; Tim Dudderidge; Ahmed El-Shater Bosaily; Eleni Frangou; Alex Freeman; Maneesh Ghei; Alastair Henderson; Richard G Hindley; Richard S Kaplan; Alex Kirkham; Robert Oldroyd; Chris Parker; Raj Persad; Shonit Punwani; Derek J Rosario; Iqbal S Shergill; Vasilis Stavrinides; Mathias Winkler; Hayley C Whitaker; Hashim U Ahmed; Mark Emberton
Journal:  Eur Urol       Date:  2020-05-01       Impact factor: 20.096

6.  Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches.

Authors:  Jun Akatsuka; Yoichiro Yamamoto; Tetsuro Sekine; Yasushi Numata; Hiromu Morikawa; Kotaro Tsutsumi; Masato Yanagi; Yuki Endo; Hayato Takeda; Tatsuro Hayashi; Masao Ueki; Gen Tamiya; Ichiro Maeda; Manabu Fukumoto; Akira Shimizu; Toyonori Tsuzuki; Go Kimura; Yukihiro Kondo
Journal:  Biomolecules       Date:  2019-10-30

7.  Histopathological features of prostate cancer conspicuity on multiparametric MRI: protocol for a systematic review and meta-analysis.

Authors:  Joseph M Norris; Lina M Carmona Echeverria; Benjamin S Simpson; Rhys Ball; Alex Freeman; Daniel Kelly; Alex Kirkham; Hayley C Whitaker; Mark Emberton
Journal:  BMJ Open       Date:  2020-10-22       Impact factor: 2.692

  7 in total

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