Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study

Conficoni, Alberto; Feraco, Paola; Mazzatenta, Diego; Zoli, Matteo; Asioli, Sofia; Zenesini, Corrado; Fabbri, Viscardo Paolo; Cellerini, M.; Bacci, A.

doi:10.1259/bjr.20200321

Cited by 18 publications

(11 citation statements)

References 37 publications

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“…To the best of our knowledge, Ugga et al and Conficoni et al both proposed methods predicting the Ki-67 index for patients with PAs based on MRI and diffusion-weighted imaging (8,10). The machine learning model built by Ugga et al showed high accuracy (91.67%, 33/36).…”

Section: Discussionmentioning

confidence: 99%

“…Obviously, applying this machine learning model in an actual clinical practice requires supporting software and platform, which must be developed in the future. Conficoni et al suggested that quantitative measures of apparent diffusion coefficient (ADC) values could predict the Ki67 value (10). However, this value was not routinely assessed in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study

et al. 2021

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BackgroundThe Ki-67 index is an indicator of proliferation and aggressive behavior in pituitary adenomas (PAs). This study aims to develop and validate a predictive nomogram for forecasting Ki-67 index levels preoperatively in PAs.MethodsA total of 439 patients with PAs underwent PA resection at the Department of Neurosurgery in Jinling Hospital between January 2018 and October 2020; they were enrolled in this retrospective study and were classified randomly into a training cohort (n = 300) and a validation cohort (n = 139). A range of clinical, radiological, and laboratory characteristics were collected. The Ki-67 index was classified into the low Ki-67 index (<3%) and the high Ki-67 index (≥3%). Least absolute shrinkage and selection operator algorithm and uni- and multivariate logistic regression analyses were applied to identify independent risk factors associated with Ki-67. A nomogram was constructed to visualize these risk factors. The receiver operation characteristic curve and calibration curve were computed to evaluate the predictive performance of the nomogram model.ResultsAge, primary-recurrence subtype, maximum dimension, and prolactin were included in the nomogram model. The areas under the curve (AUCs) of the nomogram model were 0.694 in the training cohort and 0.658 in the validation cohort. A well-fitted calibration curve was also generated for the nomogram model. A subgroup analysis revealed stable predictive performance for the nomogram model. A correlation analysis revealed that age (R = −0.23; p < 0.01), maximum dimension (R = 0.17; p < 0.01), and prolactin (R = 0.16; p < 0.01) were all significantly correlated with the Ki-67 index level.ConclusionsAge, primary-recurrence subtype, maximum dimension, and prolactin are independent predictors for the Ki-67 index level. The current study provides a novel and feasible nomogram, which can further assist neurosurgeons to develop better, more individualized treatment strategies for patients with PAs by predicting the Ki-67 index level preoperatively.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study

et al. 2021

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“…This value may reflect the growth pattern of tumors with higher invasiveness, which should be verified by further molecular studies. Ki-67 is a classical index for the description of tumor proliferation in pituitary adenomas ( 19 , 20 ). However, current estimations of Ki-67 are mostly based on visual detection under a microscope, which lacks objectivity.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the Recurrence of Non-Functioning Pituitary Adenomas Using Preoperative Supra-Intra Sellar Volume and Tumor-Carotid Distance

et al. 2021

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BackgroundCurrently, it is difficult to estimate the possibility of recurrence of nonfunctioning pituitary adenomas (NFPAs). Markers such as Ki-67 or transcription factors rely on postoperative pathology, while few indices can be used for preoperative prediction. Therefore, we aimed to investigate the predictive effectiveness of supra-intrasellar volume and tumor-carotid distance based on measurements derived from preoperative magnetic resonance imaging (MRI) data.MethodNinety-eight cases of NFPAs were evaluated, along with their clinical characteristics and MRI features. Four radiologic indices were analyzed, including intrasellar tumor volume, suprasellar tumor volume, maximum horizontal tumor diameter, and intercarotid distance. The ratio of supra-intrasellar volume and ratio of tumor-carotid distance were measured using 3D Slicer software, and the sum of two ratios was defined as the V-D value. The correlation between recurrence and multiple factors was analyzed using univariate and multivariate logistic regression and Kaplan-Meier analysis, and ROC curves were used to estimate the prognostic performance of radiologic measurements in NFPAs.ResultThe supra-intrasellar volume ratio, tumor-carotid distance ratio and V-D value were significantly correlated with the recurrence of NFPAs. The predictive importance of the V-D value reached 84.5%, with a sensitivity of 83.7% and specificity of 67.3%. The cutoff limit of the V-D value was 1.53, and patients with V-D values higher than 1.53 tended to relapse much earlier.ConclusionThe V-D value has predictive importance for the recurrence of NFPAs preoperatively. Patients with higher V-D values will undergo recurrence earlier and should be given greater consideration in terms of surgery and follow-up time.

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“…Brunetti et al have reported that machine learning analysis of preoperative T2 MRI texture-derived parameters can effectively predict the Ki-67 proliferation index category of pituitary macroadenomas [ 8 ]. Bacci et al demonstrated that the quantitative measurement of the apparent diffusion coefficient value in MRIs could be used to predict the Ki-67 index of PAs [ 9 ]. However, the implementation of these prediction methods requires specific software and platforms, and practical clinical applications are difficult to implement.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Higher Ki-67 Index in Pituitary Adenomas by Pre- and Intra-Operative Clinical Characteristics

Wang

Jiang

et al. 2022

Brain Sciences

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Objective: The Ki-67 index is an indicator of the active proliferation and aggressive behavior of pituitary adenomas (PAs). Appropriate pre- and intra-operatives of the Ki-67 index can help surgeons develop better and more personalized treatment strategies for patients with PAs. This study aimed to investigate the influence factors for predicting the Ki-67 index in PAs. Methods: Data of 178 patients with PAs confirmed by pathology were retrospectively analyzed. According to the Ki-67 index, the patients were divided into the Ki-67 < 3% and Ki-67 ≥ 3% cohorts. Patient data, including age, sex, postoperative immunohistochemical pituitary hormone positive index, Knosp grade, tumor breaking through the sellar floor, rich blood supply to the tumor, tumor located inside the sella, erosion of the dorsum sellae bone, and pituitary-specific transcription factor, were collected. A univariate logistic analysis was used to evaluate the influence factors for a high Ki-67 index. Multiple regression and receiver operating characteristic (ROC) curve were used to analyze the factors with p < 0.05. The mutant status of Ki-67 index was predicted by nomogram. Results: Multivariate regression analysis showed that rich blood supply to the tumor and erosion of the dorsum sellae bone were independent risk factors for the Ki-67 proliferation index. The ROC curves demonstrated that age, rich blood supply to the tumor, and erosion of the dorsum sellae bone can predict the occurrence of a high Ki-67 index. Together, the three risk factors provide a stronger ability to predict the Ki-67 index. The nomogram was developed and validated. Conclusion: Age, rich blood supply to the tumor, and erosion of the dorsum sellae bone are influencing factors for predicting the Ki-67 index. Suitable nomogram prediction models were developed and validated, and there is potential for personalized treatment for PA patients.

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Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study

Cited by 18 publications

References 37 publications

A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study

A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study

Prediction of the Recurrence of Non-Functioning Pituitary Adenomas Using Preoperative Supra-Intra Sellar Volume and Tumor-Carotid Distance

Prediction of Higher Ki-67 Index in Pituitary Adenomas by Pre- and Intra-Operative Clinical Characteristics

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