Negative Prostatic Biopsies in Patients with a High Risk of Prostate Cancer

Amsellem-Ouazana, Delphine; Younès, P.; Conquy, S; Peyromaure, M.; Flam, T.; Debré, B; Zerbib, Marc

doi:10.1016/j.eururo.2005.01.015

Cited by 114 publications

(8 citation statements)

References 17 publications

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“…A number of different techniques have been described for both the imaging of the prostate and the approach used to obtain the biopsy specimen. The use of T1 and T2-weighted MRI, [12] diffusion weighted MRI (MRI-DW), [13] MR spectroscopy (MRSI), [14] dynamic contrast enhanced MRI (DCE-MRI), [15] and various combinations of the above in multi-parametric imaging protocols [16]–[18] have all been described for the identification of prostatic lesions for targeted biopsy. The biopsy specimen can then be obtained by using transrectal, [12] transperineal, [19] or transgluteal approaches [20].…”

Section: Introductionmentioning

confidence: 99%

Repeat Prostate Biopsy Strategies after Initial Negative Biopsy: Meta-Regression Comparing Cancer Detection of Transperineal, Transrectal Saturation and MRI Guided Biopsy

et al. 2013

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IntroductionThere is no consensus on how to investigate men with negative transrectal ultrasound guided prostate biopsy (TRUS-B) but ongoing suspicion of cancer. Three strategies used are transperineal (TP-B), transrectal saturation (TS-B) and MRI-guided biopsy (MRI-B). We compared cancer yields of these strategies.MethodsPapers were identified by search of Pubmed, Embase and Ovid Medline. Included studies investigated biopsy diagnostic yield in men with at least one negative TRUS-B and ongoing suspicion of prostate cancer. Data including age, PSA, number of previous biopsy episodes, number of cores at re-biopsy, cancer yield, and Gleason score of detected cancers were extracted. Meta-regression analyses were used to analyse the data.ResultsForty-six studies were included; 12 of TS-B, 14 of TP-B, and 20 of MRI-B, representing 4,657 patients. Mean patient age, PSA and number of previous biopsy episodes were similar between the strategies. The mean number of biopsy cores obtained by TP-B and TS-B were greater than MRI-B. Cancer detection rates were 30·0%, 36·8%, and 37·6% for TS-B, TP-B, and MRI-B respectively. Meta-regression analysis showed that MRI-B had significantly higher cancer detection than TS-B. There were no significant differences however between MRI-B and TP-B, or TP-B and TS-B. In a sensitivity analysis incorporating number of previous biopsy episodes (36 studies) the difference between MRI-B and TP-B was not maintained resulting in no significant difference in cancer detection between the groups. There were no significant differences in median Gleason scores detected comparing the three strategies.ConclusionsIn the re-biopsy setting, it is unclear which strategy offers the highest cancer detection rate. MRI-B may potentially detect more prostate cancers than other modalities and can achieve this with fewer biopsy cores. However, well–designed prospective studies with standardised outcome measures are needed to accurately compare modalities and define an optimum re-biopsy approach.

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Section: Introductionmentioning

confidence: 99%

Repeat Prostate Biopsy Strategies after Initial Negative Biopsy: Meta-Regression Comparing Cancer Detection of Transperineal, Transrectal Saturation and MRI Guided Biopsy

et al. 2013

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“…Therefore, there is a consensus that a combination of markers can increase the diagnostic specificity and sensitivity than single marker (Schneider et al, 2002;Farlow et al, 2010;Wang et al, 2013;Li et al, 2014). For example, in diagnosing some malignant tumors, such as prostate or ovarian cancer, by combining multiple biomarkers data, the diagnostic performance was improved rapidly than individual serum biomarker (Amsellem-Ouazana et al, 2005;Kim et al, 2012). In recent years, people have done a lot of researches on statistical point of view, hoping to establish a kind of intelligent diagnosis model based on multiple tumor marker detection data, in order to overcome the influence of some subjective and individual factors, and improve the further tumor diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Chen²,

Cui³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool -gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are requentlyused lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.

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“…22 studies [ 12 , 17 , 41 , 44 , 45 , 52 , 61 – 76 ] involving 2294 patients reported the diagnostic accuracy of T2-MRI. 8 studies [ 17 , 43 , 52 , 65 , 68 , 75 – 77 ] involving 1445 patients reported the diagnostic accuracy of DCE-MRI.…”

Section: Resultsmentioning

confidence: 99%

“…Early promising data suggest that multimodal, also referred to as multiparametric MRI, combining several MRI modalities such as morphologic T2-MRI sequences and DCE-MRI or DWI and/or MRSI (functional MRI modalities), may be of additional value for the localization of prostate cancer and guidance of biopsy [ 12 , 13 , 41 – 61 ]. The combined use of T2-MRI and functional MRI modalities (DCE-MRI, DWI and/or MRSI) has been shown to improve cancer localization [ 53 , 54 ] and cancer volume measurement [ 47 ] in the peripheral zone.…”

Section: Introductionmentioning

confidence: 99%

A systematic review on multiparametric MR imaging in prostate cancer detection

Fusco

Sansone

Granata

et al. 2017

Infect Agents Cancer

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BackgroundLiterature data suggest that multi-parametric Magnetic Resonance Imaging (MRI), including morphologic T2-weigthed images (T2-MRI) and functional approaches such as Dynamic Contrast Enhanced-MRI (DCE-MRI), Diffusion Weighted Imaging (DWI) and Magnetic Resonance Spectroscopic Imaging (MRSI), give an added value in the prostate cancer localization and local staging.MethodsWe performed a systematic review of literature about the role and the potentiality of morphological and functional MRI in prostate cancer, also in a multimodal / multiparametric approach, and we reported the diagnostic accuracy results for different imaging modalities and for different MR coil settings: endorectal coil (ERC) and phased array coil (PAC). Forest plots and receiver operating characteristic curves were performed. Risk of bias and the applicability at study level were calculated.ResultsThirty three papers were identified for the systematic review. Sensitivity and specificity values were, respectively, for T2-MRI of 75% and of 60%, for DCE-MRI of 80% and of 72%, for MRSI of 89% and of 69%, for combined T2-MRI and DCE-MRI of 87% and of 46%, for combined T2-MRI and MRSI of 79% and of 57%, for combined T2-MRI, DWI and DCE-MRI of 81% and of 84%, and for combined MRSI and DCE-MRI of 83% and of 83%. For MRI studies performed with ERC we obtained a pooled sensitivity and specificity of 81% and of 66% while the pooled values for MRI studies performed with PAC were of 78% and of 64%, respectively (p>0.05 at McNemar test). No studies were excluded from the analysis based on the quality assessment.ConclusionsERC use yielded no additional benefit in terms of prostate cancer detection accuracy compared to multi-channel PAC use (71% versus 68%) while the use of additional functional imaging techniques (DCE-MRI, DWI and MRSI) in a multiparametric MRI protocol improves the accuracy of prostate cancer detection allowing both the early cure and the guidance of biopsy.

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Negative Prostatic Biopsies in Patients with a High Risk of Prostate Cancer

Cited by 114 publications

References 17 publications

Repeat Prostate Biopsy Strategies after Initial Negative Biopsy: Meta-Regression Comparing Cancer Detection of Transperineal, Transrectal Saturation and MRI Guided Biopsy

Repeat Prostate Biopsy Strategies after Initial Negative Biopsy: Meta-Regression Comparing Cancer Detection of Transperineal, Transrectal Saturation and MRI Guided Biopsy

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

A systematic review on multiparametric MR imaging in prostate cancer detection

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