Meta-analysis of predictive models to assess the clinical validity and utility for patient-centered medical decision making: application to the CAncer of the Prostate Risk Assessment (CAPRA)

Lorent, Marine; Maalmi, Haïfa; Tessier, Philippe; Supiot, S.; Dantan, Étienne; Foucher, Yohann

doi:10.1186/s12911-018-0727-2

Cited by 14 publications

(13 citation statements)

References 57 publications

(67 reference statements)

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“…Despite the high value of the Gleason score for prognosis, there is considerable evidence that patients with the same Gleason score may experience vastly different clinical outcomes [22,23,24]. As one of the most powerful predictive scoring systems, CAPRAS was found to lack sufficient prediction accuracy in two meta-analyses [25,26]. Therefore, it is hoped that new tissue-based molecular biomarkers could be potential candidates to improve the disease recurrence prediction [27,28,29,30].…”

Section: Introductionmentioning

confidence: 99%

A Novel Predictor Tool of Biochemical Recurrence after Radical Prostatectomy Based on a Five-MicroRNA Tissue Signature

Zhi

Weickmann

Jung

et al. 2019

Cancers

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Within five to ten years after radical prostatectomy (RP), approximately 15–34% of prostate cancer (PCa) patients experience biochemical recurrence (BCR), which is defined as recurrence of serum levels of prostate-specific antigen >0.2 µg/L, indicating probable cancer recurrence. Models using clinicopathological variables for predicting this risk for patients lack accuracy. There is hope that new molecular biomarkers, like microRNAs (miRNAs), could be potential candidates to improve risk prediction. Therefore, we evaluated the BCR prognostic capability of 20 miRNAs, which were selected by a systematic literature review. MiRNA expressions were measured in formalin-fixed, paraffin-embedded (FFPE) tissue RP samples of 206 PCa patients by RT-qPCR. Univariate and multivariate Cox regression analyses were performed, to assess the independent prognostic potential of miRNAs. Internal validation was performed, using bootstrapping and the split-sample method. Five miRNAs (miR-30c-5p/31-5p/141-3p/148a-3p/miR-221-3p) were finally validated as independent prognostic biomarkers. Their prognostic ability and accuracy were evaluated using C-statistics of the obtained prognostic indices in the Cox regression, time-dependent receiver-operating characteristics, and decision curve analyses. Models of miRNAs, combined with relevant clinicopathological factors, were built. The five-miRNA-panel outperformed clinically established BCR scoring systems, while their combination significantly improved predictive power, based on clinicopathological factors alone. We conclude that this miRNA-based-predictor panel will be worth to be including in future studies.

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Section: Introductionmentioning

confidence: 99%

A Novel Predictor Tool of Biochemical Recurrence after Radical Prostatectomy Based on a Five-MicroRNA Tissue Signature

Zhi

Weickmann

Jung

et al. 2019

Cancers

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“…В независимой ВВ ВИЗГ подтвердил свое преимущество перед перечисленными маркерами и алгоритмами в точности различать клинически значимые группы больных (рТ2 vs рТ3, рГл <7 vs рГл ≥7, индолентный vs агрессивный РПЖ). 63,20 (58,06-68,35) 74,70 (65,35-84,05) 25,35 (19,51-31,19) 19,35 (9,52-29,19) 95,16 (91,38-98,94) 85,71 (70,75-100) 90,00 (82,59) 80,00 (59,76-100) 42,60 (36,78-48,42) 26,47 (15,98-36,96) 51,04 (45,38) 36,14 (25,81-46,48) 6 % 63,20 (58,06-68,35) 74,70 (65,05) 12,68 (8,14) 3,23 (0 87,24 (83,68-90,80) 89,16 (82,47-95,85) 10,54 (7,(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)05) 10,81 (3,89) 97,67 (93, 66,67 (35,(87)(88)(89)(90)(91)(92)(93)(94)(95)(96)(97)47) 96,88 (90,85-100) 72,73 (46,05) 13,77…”

Section: Discussionunclassified

“…Известен алгоритм CAPRA, включающий, кроме лабораторных, клинико-морфологические данные, сконструированный для долечебного уточнения стадии РПЖ, который продемонстрировал несколько лучшее совпадение с последующими послеоперационными данными, чем ПСА и ассоциированные маркеры [6].…”

Section: Introductionunclassified

The validation of threshold decision ruls and calculator for APhiG algoritm for clarification of prostate cancer staging before treatment

et al. 2020

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Background. We have previously described an algorithm APhiG (Age of patients, Prostate health index and Gleason score), for staging of prostate cancer before treatment. The algorithm was developed by logistic regression on a training dataset and validated on a validation dataset (VD). Objective. Validation of threshold decision rules and a program for APhiG calculation on the VD.Materials and methods. ROC curve analysis on VD (83 cases).Results and conclusion. It was shown that sensitivity, specificity, positive and negative predictive value, diagnostic accuracy threshold decision rules and area under the curve (AUC) for APhiG in the VD (n = 83) not significantly different from those indicators in the training dataset (n = 337), which was the basis for the algorithm APhiG development.

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“…Although statistically significant differences in BCR rates between the three different CAPRA-S risk groups have been validated in multiple cohorts [ 5 – 8 , 20 , 21 ], heterogeneity of clinical outcome is still observed among the high-risk CAPRA-S group. As such, while some patients with high-risk CAPRA-S scores may benefit from multimodal therapy up-front, a proportion of these high-risk patients will not go on to develop BCR or metastasis, and will experience unnecessary morbidity from early treatment [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

A 38-gene model comprised of key TET2-associated genes shows additive utility to high-risk prostate cancer cases in the prognostication of biochemical recurrence

2020

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Background Early treatment of patients at risk for developing aggressive prostate cancer is able to delay metastasis and reduce mortality; as such, up-front identification of these patients is critical. Several risk classification systems, including CAPRA-S, are currently used for disease prognostication. However, high-risk patients identified by these systems can still exhibit wide-ranging disease outcomes, leading to overtreatment of some patients in this group. Methods The master methylation regulator TET2 is downregulated in prostate cancer, where its loss is linked to aggressive disease and poor outcome. Using a random forest strategy, we developed a model based on the expression of 38 genes associated with TET2 utilizing 100 radical prostatectomy samples (training cohort) with a 49% biochemical recurrence rate. This 38-gene model was comprised of both upregulated and downregulated TET2-associated genes with a binary outcome, and was further assessed in an independent validation (n = 423) dataset for association with biochemical recurrence. Results 38-gene model status was able to correctly identify patients exhibiting recurrence with 81.4% sensitivity in the validation cohort, and added significant prognostic utility to the high-risk CAPRA-S classification group. Patients considered high-risk by CAPRA-S with negative 38-gene model status exhibited no statistically significant difference in time to recurrence from low-risk CAPRA-S patients, indicating that the expression of TET2-associated genes is able to separate truly high-risk cases from those which have a more benign disease course. Conclusions The 38-gene model may hold potential in determining which patients would truly benefit from aggressive treatment course, demonstrating a novel role for genes linked to TET2 in the prognostication of PCa and indicating the importance of TET2 dysregulation among high-risk patient groups.

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Meta-analysis of predictive models to assess the clinical validity and utility for patient-centered medical decision making: application to the CAncer of the Prostate Risk Assessment (CAPRA)

Cited by 14 publications

References 57 publications

A Novel Predictor Tool of Biochemical Recurrence after Radical Prostatectomy Based on a Five-MicroRNA Tissue Signature

A Novel Predictor Tool of Biochemical Recurrence after Radical Prostatectomy Based on a Five-MicroRNA Tissue Signature

The validation of threshold decision ruls and calculator for APhiG algoritm for clarification of prostate cancer staging before treatment

A 38-gene model comprised of key TET2-associated genes shows additive utility to high-risk prostate cancer cases in the prognostication of biochemical recurrence

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