Apparent diffusion coefficient and pituitary macroadenomas: pre-operative assessment of tumor atypia

Tamrazi, Benita; Pekmezci, Melike; Aboian, Mariam; Tihan, Tarık; Glastonbury, Christine M.

doi:10.1007/s11102-016-0759-5

Cited by 27 publications

(12 citation statements)

References 21 publications

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“…4,5 DWI and low ADC values are suggested as the predictors of aggressive pituitary macro-adenoma. 31 In the present study, we have seen that ADC values of histogram features including maximum, mean, median, uniformity, entropy, kurtosis and 25th percentile were lower in functional macro-adenomas compared to non-functional; however, from this feature, only uniformity had a significant result (Table 2; ADC mean±SD values). Further, smoothness was meaningfully lower in the functional group than non-functional (Table 2).…”

Section: Discussionsupporting

confidence: 53%

Accuracy of diffusion-weighted imaging-magnetic resonance in differentiating functional from non-functional pituitary macro-adenoma and classification of tumor consistency

et al. 2018

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The purpose of this study was to determine the accuracy of selected first or second-order histogram features in differentiation of functional types of pituitary macro-adenomas. Materials and methods: Diffusion-weighted imaging magnetic resonance imaging was performed on 32 patients (age meanAEstandard deviation ¼ 43.09 AE 11.02 years; min ¼ 22 and max ¼ 65 years) with pituitary macro-adenoma (10 with functional and 22 with non-functional tumors). Histograms of apparent diffusion coefficient were generated from regions of interest and selected first or second-order histogram features were extracted. Collagen contents of the surgically resected tumors were examined histochemically using Masson trichromatic staining and graded as containing <1%, 1-3%, and >3% of collagen. Results: Among selected first or second-order histogram features, uniformity (p ¼ 0.02), 75th percentile (p ¼ 0.03), and tumor smoothness (p ¼ 0.02) were significantly different between functional and non-functional tumors. Tumor smoothness > 5.7 Â 10 À9 (area under the curve ¼ 0.75; 0.56-0.89) had 70% (95% confidence interval ¼ 34.8-93.3%) sensitivity and 33.33% (95% confidence interval ¼ 14.6-57.0%) specificity for diagnosis of functional tumors. Uniformity 179.271 had a sensitivity of 60% (95% confidence interval ¼ 26.2-87.8%) and specificity of 90.48% (95% confidence interval ¼ 69.6-98.8%) with area under the curve ¼ 0.76; 0.57-0.89. The 75th percentile >0.7 had a sensitivity of 80% (95% confidence interval ¼ 44.4-97.5%) and specificity of 66.67% (95% confidence interval ¼ 43.0-85.4%) for categorizing tumors to functional and non-functional types (area under the curve ¼ 0.74; 0.55-0.88). Using these cutoffs , smoothness and uniformity are suggested as negative predictive indices (non-functional tumors) whereas 75th percentile is more applicable for diagnosis of functional tumors. Conclusion: First or second-order histogram features could be helpful in differentiating functional vs non-functional pituitary macro-adenoma tumors.

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Section: Discussionsupporting

confidence: 53%

Accuracy of diffusion-weighted imaging-magnetic resonance in differentiating functional from non-functional pituitary macro-adenoma and classification of tumor consistency

et al. 2018

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“…Although they suggested that ADC values were useful for differentiation of the consistency and tumor atypia of pituitary adenomas, their conclusions were not different from our results. [17][18][19] In another report using periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) DWI reported that ADC parameters including ADC min , ADC mean , ADC max were not correlated with the MIB-1 index. 20 To date, histogram analysis of ADC has not been done to evaluate the aggressiveness of pituitary adenomas, therefore this study is the first report to evaluate the aggressiveness of pituitary adenomas using ADC histogram analysis.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Pituitary macroadenoma: Accuracy of apparent diffusion coefficient magnetic resonance imaging in grading tumor aggressiveness

et al. 2019

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The purpose of this study is to evaluate the accuracy of apparent diffusion coefficient magnetic resonance imaging in grading tumor aggressiveness using histogram apparent diffusion coefficient values. Materials and methods: Eighteen patients with surgically proved pituitary macroadenomas were included in this study. Diffusion-weighted imaging with single-shot echo-planar sequence at 3-T with a 32-channel head coil was performed with b values of 0 and 1000 s/mm 2. Calculated apparent diffusion coefficient maps were generated, and a 3-D volume of interest was placed on the tumor while superimposing contrast-enhanced magnetic resonance images. All apparent diffusion coefficient values within the volume of interest were used to compute the average apparent diffusion coefficient of the tumor. The apparent diffusion coefficient values were binned to construct the apparent diffusion coefficient histogram. Using the histogram, the mean, percentiles, skewness, and kurtosis of the apparent diffusion coefficient of the entire tumor were computed. Apparent diffusion coefficient histogram parameters were compared with the MIB-1 index, invasiveness, and recurrence for grading tumor aggressiveness of pituitary adenomas. Results: The skewness of the apparent diffusion coefficient histogram only showed significant differences among MIB-1 indices (p ¼ 0.030). All apparent diffusion coefficient histogram parameters showed no significant differences between negative and positive invasion. The skewness and kurtosis of the apparent diffusion coefficient histogram showed significant differences between positive and negative recurrence (skewness p ¼ 0.011, kurtosis p ¼ 0.011). Receiver-operating characteristics analysis between positive and negative recurrence showed that both skewness and kurtosis of the apparent diffusion coefficient achieved area under the curve at 0.967. Conclusion: Skewness and kurtosis of the apparent diffusion coefficient histogram were the predictive parameters for assessing tumor proliferative potential and recurrence of pituitary adenomas.

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“…Recently, low apparent diffusion coefficient (ADC) on diffusion-weight MR imaging (DWI), indicating a higher cellular density, is reported to be associated with tumor progression in NFPAs (26,39). However, the ADC values may be affected by susceptibility artifacts from blood products because of apoplexy or necrosis; therefore, they could only be measured for solid NFPAs without hemorrhage or cystic changes (9,26,40).…”

Section: Discussionmentioning

confidence: 99%

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas

Zhang

Chen

et al. 2020

Front. Oncol.

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ObjectivesA subset of non-functioning pituitary macroadenomas (NFPAs) may exhibit early progression/recurrence (P/R) after surgical resection. The purpose of this study was to apply radiomics in predicting P/R in NFPAs.MethodsOnly patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year were included in this study. From September 2010 to December 2017, 50 eligible patients diagnosed with pathologically confirmed NFPAs were identified. Preoperative coronal T2WI and contrast-enhanced (CE) T1WI imaging were analyzed by computer algorithms. For each imaging sequence, 32 first-order features and 75 texture features were extracted. Support vector machine (SVM) classifier was utilized to evaluate the importance of extracted parameters, and the most significant three parameters were used to build the prediction model. The SVM score was calculated based on the three selected features.ResultsTwenty-eight patients exhibited P/R (28/50, 56%) after surgery. The median follow-up time was 38 months, and the median time to P/R was 20 months. Visual disturbance, hypopituitarism, extrasellar extension, compression of the third ventricle, large tumor height and volume, failed optic chiasmatic decompression, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, symptoms of sex hormones, hypopituitarism, and SVM score were high risk factors for P/R (p < 0.05) with hazard ratios of 10.71, 2.68, and 6.88. The three selected radiomics features were T1 surface-to-volume radio, T1 GLCM-informational measure of correlation, and T2 NGTDM-coarseness. The radiomics predictive model shows 25 true positive, 16 true negative, 6 false positive, and 3 false negative cases, with an accuracy of 82% and AUC of 0.78 in differentiating P/R from non-P/R NFPAs. For SVM score, optimal cut-off value of 0.537 and AUC of 0.87 were obtained for differentiation of P/R. Higher SVM scores were associated with shorter progression-free survival (p < 0.001).ConclusionsOur preliminary results showed that objective and quantitative MR radiomic features can be extracted from NFPAs. Pending more studies and evidence to support the findings, radiomics analysis of preoperative MRI may have the potential to offer valuable information in treatment planning for NFPAs.

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Apparent diffusion coefficient and pituitary macroadenomas: pre-operative assessment of tumor atypia

Cited by 27 publications

References 21 publications

Accuracy of diffusion-weighted imaging-magnetic resonance in differentiating functional from non-functional pituitary macro-adenoma and classification of tumor consistency

Accuracy of diffusion-weighted imaging-magnetic resonance in differentiating functional from non-functional pituitary macro-adenoma and classification of tumor consistency

Pituitary macroadenoma: Accuracy of apparent diffusion coefficient magnetic resonance imaging in grading tumor aggressiveness

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas

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