Preoperative nomograms incorporating magnetic resonance imaging and spectroscopy for prediction of insignificant prostate cancer

被引:88
|
作者
Shukla-Dave, Amita [1 ,2 ]
Hricak, Hedvig [2 ]
Akin, Oguz [2 ]
Yu, Changhong [4 ]
Zakian, Kristen L. [1 ,2 ]
Udo, Kazuma [3 ,5 ,6 ]
Scardino, Peter T. [3 ]
Eastham, James [3 ]
Kattan, Michael W. [4 ]
机构
[1] Mem Sloan Kettering Canc Ctr, Dept Med Phys, New York, NY 10065 USA
[2] Mem Sloan Kettering Canc Ctr, Dept Radiol, New York, NY 10065 USA
[3] Mem Sloan Kettering Canc Ctr, Dept Urol, New York, NY 10065 USA
[4] Cleveland Clin, Dept Quantitat Hlth Sci, Cleveland, OH 44106 USA
[5] Saga Univ, Fac Med, Dept Urol, Saga 840, Japan
[6] Saga Univ, Fac Med, Dept Urol & Microbiol, Saga 840, Japan
关键词
magnetic resonance imaging; magnetic resonance spectroscopic imaging; nomograms; prostate neoplasms; RADICAL PROSTATECTOMY; BIOPSY; MEN; ADENOCARCINOMA; FRAGMENTATION; SUBMISSION; MANAGEMENT; FEATURES; UTILITY; TRENDS;
D O I
10.1111/j.1464-410X.2011.10612.x
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
OBJECTIVES To validate previously published nomograms for predicting insignificant prostate cancer (PCa) that incorporate clinical data, percentage of biopsy cores positive (%BC+) and magnetic resonance imaging (MRI) or MRI/MR spectroscopic imaging (MRSI) results. We also designed new nomogram models incorporating magnetic resonance results and clinical data without detailed biopsy data. Nomograms for predicting insignificant PCa can help physicians counsel patients with clinically low-risk disease who are choosing between active surveillance and definitive therapy. PATIENTS AND METHODS In total, 181 low-risk PCa patients (clinical stage T1c-T2a, prostate-specific antigen level <10 ng/mL, biopsy Gleason score of 6) had MRI/MRSI before surgery. For MRI and MRI/MRSI, the probability of insignificant PCa was recorded prospectively and independently by two radiologists on a scale from 0 (definitely insignificant) to 3 (definitely significant PCa). Insignificant PCa was defined on surgical pathology. There were four models incorporating MRI or MRI/MRSI and clinical data with and without %BC+ that were compared with a base clinical model without %BC and a more comprehensive clinical model with %BC+. Prediction accuracy was assessed using areas under receiver operator characteristic curves. RESULTS At pathology, 27% of patients had insignificant PCa, and the Gleason score was upgraded in 56.4% of patients. For both readers, all magnetic resonance models performed significantly better than the base clinical model (P <= 0.05 for all) and similarly to the more comprehensive clinical model. CONCLUSIONS Existing models incorporating magnetic resonance data, clinical data and %BC+ for predicting the probability of insignificant PCa were validated. All MR-inclusive models performed significantly better than the base clinical model.
引用
收藏
页码:1315 / 1322
页数:8
相关论文
共 50 条
  • [1] Prostate Magnetic Resonance Imaging Analyses, Clinical Parameters, and Preoperative Nomograms in the Prediction of Extraprostatic Extension
    Majchrzak, Natalia
    Cieslinski, Piotr
    Glyda, Maciej
    Karmelita-Katulska, Katarzyna
    CLINICS AND PRACTICE, 2021, 11 (04) : 763 - 774
  • [2] The utility of magnetic resonance imaging and spectroscopy for predicting insignificant prostate cancer: an initial analysis
    Shukla-Dave, Amita
    Hricak, Hedvig
    Kattan, Michael W.
    Pucar, Darko
    Kuroiwa, Kentaro
    Chen, Hui-Ni
    Spector, Jessica
    Koutcher, Jason A.
    Zakian, Kristen L.
    Scardino, Peter T.
    BJU INTERNATIONAL, 2007, 99 (04) : 786 - 793
  • [3] Integration of magnetic resonance imaging into prostate cancer nomograms
    Brinkley, Garrett J.
    Fang, Andrew M.
    Rais-Bahrami, Soroush
    THERAPEUTIC ADVANCES IN UROLOGY, 2022, 14
  • [4] Preoperative prediction of insignificant prostate cancer in the Illawarra area
    Osgood, L.
    BJU INTERNATIONAL, 2008, 101 : 22 - 22
  • [5] The definition and preoperative prediction of clinically insignificant prostate cancer
    Dugan, JA
    Bostwick, DG
    Myers, RP
    Qian, JQ
    Bergstralh, EJ
    Oesterling, JE
    JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 1996, 275 (04): : 288 - 294
  • [6] Diffusion-weighted magnetic resonance imaging for prediction of insignificant prostate cancer in potential candidates for active surveillance
    Kim, Tae Heon
    Jeong, Jae Yong
    Lee, Sin Woo
    Kim, Chan Kyo
    Park, Byung Kwan
    Sung, Hyun Hwan
    Jeon, Hwang Gyun
    Jeong, Byong Chang
    Seo, Seong Il
    Lee, Hyun Moo
    Choi, Han Yong
    Jeon, Seong Soo
    EUROPEAN RADIOLOGY, 2015, 25 (06) : 1786 - 1792
  • [7] Diffusion-weighted magnetic resonance imaging for prediction of insignificant prostate cancer in potential candidates for active surveillance
    Tae Heon Kim
    Jae Yong Jeong
    Sin Woo Lee
    Chan Kyo Kim
    Byung Kwan Park
    Hyun Hwan Sung
    Hwang Gyun Jeon
    Byong Chang Jeong
    Seong Il Seo
    Hyun Moo Lee
    Han Yong Choi
    Seong Soo Jeon
    European Radiology, 2015, 25 : 1786 - 1792
  • [8] Role of magnetic resonance imaging for preoperative prediction of early biochemical failure in localized prostate cancer
    Cassin, Jeremy
    Walker, Paul Michael
    Blanc, Julie
    Asuncion, Audrey
    Bardet, Florian
    Cormier, Luc
    Loffroy, Romaric
    Cochet, Alexandre
    QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2023, 13 (03) : 1440 - 1452
  • [9] Preoperative and Postoperative Nomograms Incorporating Surgeon Experience for Clinically Localized Prostate Cancer
    Kattan, Michael W.
    Vickers, Andrew J.
    Yu, Changhong
    Bianco, Fernando J.
    Cronin, Angel M.
    Eastham, James A.
    Klein, Eric A.
    Reuther, Alwyn M.
    Pontes, Jose Edson
    Scardino, Peter T.
    CANCER, 2009, 115 (05) : 1005 - 1010
  • [10] Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer
    Ma, Shuai
    Xie, Huihui
    Wang, Huihui
    Yang, Jiejin
    Han, Chao
    Wang, Xiaoying
    Zhang, Xiaodong
    MOLECULAR IMAGING AND BIOLOGY, 2020, 22 (03) : 711 - 721