Combined Diagnosis of Whole-Lesion Histogram Analysis of T1- and T2-Weighted Imaging for Differentiating Adrenal Adenoma and Pheochromocytoma: A Support Vector Machine-Based Study

Ensemble Machine Learning Model Incorporating Radiomics and Body Composition for Predicting Intraoperative HDI in PPGL

CONTEXT

Intraoperative hemodynamic instability (HDI) can lead to cardiovascular and cerebrovascular complications during surgery for pheochromocytoma/paraganglioma (PPGL).

OBJECTIVES

We aimed to assess the risk of intraoperative HDI in patients with PPGL to improve surgical outcome.

METHODS

A total of 199 consecutive patients with PPGL confirmed by surgical pathology were retrospectively included in this study. This cohort was separated into 2 groups according to intraoperative systolic blood pressure, the HDI group (n = 101) and the hemodynamic stability (HDS) group (n = 98). It was also divided into 2 subcohorts for predictive modeling: the training cohort (n = 140) and the validation cohort (n = 59). Prediction models were developed with both the ensemble machine learning method (EL model) and the multivariate logistic regression model using body composition parameters on computed tomography, tumor radiomics, and clinical data. The efficiency of the models was evaluated with discrimination, calibration, and decision curves.

RESULTS

The EL model showed good discrimination between the HDI group and HDS group, with an area under the curve of (AUC) of 96.2% (95% CI, 93.5%-99.0%) in the training cohort, and an AUC of 93.7% (95% CI, 88.0%-99.4%) in the validation cohort. The AUC values from the EL model were significantly higher than the logistic regression model, which had an AUC of 74.4% (95% CI, 66.1%-82.6%) in the training cohort and an AUC of 74.2% (95% CI, 61.1%-87.3%) in the validation cohort. Favorable calibration performance and clinical applicability of the EL model were observed.

CONCLUSION

The EL model combining preoperative computed tomography-based body composition, tumor radiomics, and clinical data could potentially help predict intraoperative HDI in patients with PPGL.

An Integrated CT and MRI Imaging Model to Differentiate between Adrenal Adenomas and Pheochromocytomas

PURPOSE

to perform an external validation of our predictive model to rule out pheochromocytoma (PHEO) based on unenhanced CT in a cohort of patients with PHEOs and adenomas who underwent adrenalectomy.

METHODS

The predictive model was previously developed in a retrospective cohort of 1131 patients presenting with adrenal lesions. In the present study, we performed an external validation of the model in another cohort of 214 patients with available histopathological results.

RESULTS

For the external validation, 115 patients with PHEOs and 99 with adenomas were included. Our previously described predictive model combining the variables of high lipid content and tumor size in unenhanced CT (AUC-ROC: 0.961) had a lower diagnostic accuracy in our current study population for the prediction of PHEO (AUC: 0.750). However, when we excluded atypical adenomas (with Hounsfield units (HU) > 10, n = 39), the diagnostic accuracy increased to 87.4%. In addition, in the whole cohort (including atypical adenomas), when MRI information was included in the model, the diagnostic accuracy increased to up to 85% when the variables tumor size, high lipid content in an unenhanced CT scan, and hyperintensity in the T2 sequence in MRI were included. The probability of PHEO was <0.3% for adrenal lesions <20 mm with >10 HU and without hyperintensity in T2.

CONCLUSION

Our study confirms that our predictive model combining tumor size and lipid content has high reliability for the prediction of PHEO when atypical adrenal lesions are excluded. However, for atypical adrenal lesions with >10 HU in an unenhanced CT scan, MRI information is necessary for a proper exclusion of the PHEO diagnosis.

Dual-energy spectral detector computed tomography differential diagnosis of adrenal adenoma and pheochromocytoma: Changes in the energy level curve, a phenomenon caused by lipid components?

Background and objectives

Pheochromocytoma and adrenal adenoma are common space-occupying lesions of the adrenal gland, and incorrect surgery may lead to adrenal crisis. We used a new method, dual-energy spectral detector computed tomography (SDCT), to differentiate between the two.

Materials and methods

We analysed the imaging images of patients with SDCT scans and pathologically confirmed adrenal adenomas (n=70) and pheochromocytomas (n=15). The 40, 70, and 100 KeV virtual monoenergetic images (VMIs) were reconstructed based on the SCDT arterial phase, and the correlation between the arterial/venous phase iodine concentration (AP-IC/VP-IC), the effective atomic number (Z-effect), the slope of the Hounsfield unit attenuation plot (VMI slope) and the pathological results was tested. The Shapiro-Wilk test was used to determine whether the above data conformed to a normal distribution. For parameters with P greater than 0.05, Student's t test was used, and the Mann-Whitney test was used for the remaining parameters. A ROC curve was drawn based on the results.

Results

Student's t test showed that the 40 KeV VMI and the VMI slope were both statistically significant (P<0.01). The Mann-Whitney U test showed that ID-A was statistically significant (P=0.004). ROC curve analysis showed that 40 keV VMI (AUC=0.818), AP-IC (AUC=0.736), difference (AUC=0.817) and VMI-Slope (0.817) could be used to differentiate adrenal adenoma from pheochromocytoma.

Conclusion

The effect of lipid components on SDCT parameters can be used to differentiate adrenal adenoma from pheochromocytoma.

Artificial intelligence in adrenal imaging: A critical review of current applications

In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps from detection to analysis does not exist yet, multiple AI algorithms have been developed and tested with encouraging results. However, AI in this setting is still at an early stage. In this regard, most published studies about AI in adrenal gland imaging report preliminary results that do not have yet daily applications in clinical practice. In this review, recent developments and current results of AI in the field of adrenal imaging are presented. Limitations and future perspectives of AI are discussed.

Diagnostic performance of radiomics in adrenal masses: A systematic review and meta-analysis

Objectives

(1) To assess the methodological quality and risk of bias of radiomics studies investigating the diagnostic performance in adrenal masses and (2) to determine the potential diagnostic value of radiomics in adrenal tumors by quantitative analysis.

Methods

PubMed, Embase, Web of Science, and Cochrane Library databases were searched for eligible literature. Methodological quality and risk of bias in the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS). The diagnostic performance was evaluated by pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Spearman’s correlation coefficient and subgroup analysis were used to investigate the cause of heterogeneity. Publication bias was examined using the Deeks’ funnel plot.

Results

Twenty-eight studies investigating the diagnostic performance of radiomics in adrenal tumors were identified, with a total of 3579 samples. The average RQS was 5.11 (14.2% of total) with an acceptable inter-rater agreement (ICC 0.94, 95% CI 0.93–0.95). The risk of bias was moderate according to the result of QUADAS-2. Nine studies investigating the use of CT-based radiomics in differentiating malignant from benign adrenal tumors were included in the quantitative analysis. The pooled sensitivity, specificity, DOR and AUC with 95% confidence intervals were 0.80 (0.68-0.88), 0.83 (0.73-0.90), 19.06 (7.87-46.19) and 0.88 (0.85–0.91), respectively. There was significant heterogeneity among the included studies but no threshold effect in the meta-analysis. The result of subgroup analysis demonstrated that radiomics based on unenhanced and contrast-enhanced CT possessed higher diagnostic performance, and second-order or higher-order features could enhance the diagnostic sensitivity but also increase the false positive rate. No significant difference in diagnostic ability was observed between studies with machine learning and those without.

Conclusions

The methodological quality and risk of bias of studies investigating the diagnostic performance of radiomics in adrenal tumors should be further improved in the future. CT-based radiomics has the potential benefits in differentiating malignant from benign adrenal tumors. The heterogeneity between the included studies was a major limitation to obtaining more accurate conclusions.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/ CRD 42022331999 .

Evaluation of the T2-weighted (T2W) adrenal MRI calculator to differentiate adrenal pheochromocytoma from lipid-poor adrenal adenoma

OBJECTIVES

To evaluate the T2-weighted (T2W) MRI calculator to differentiate adrenal pheochromocytoma from lipid-poor adrenal adenoma.

METHODS

Twenty-nine consecutive pheochromocytomas resected between 2010 and 2019 were compared to 23 consecutive lipid-poor adrenal adenomas. Three blinded radiologists (R1, R2, R3) subjectively evaluated T2W signal intensity and heterogeneity and extracted T2W signal intensity ratio (SIR) and entropy. These values were imputed into a quantitative and qualitative T2W adrenal MRI calculator (logistic regression model encompassing T2W SIR + entropy and subjective SI [relative to renal cortex] and heterogeneity) using a predefined threshold to differentiate metastases from adenoma and accuracy derived by a 2 × 2 table analysis.

RESULTS

Subjectively, pheochromocytomas were brighter (p < 0.001) and more heterogeneous (p < 0.001) for all three radiologists. Inter-observer agreement was fair-to-moderate for T2W signal intensity (K = 0.37-0.46) and fair for heterogeneity (K = 0.24-0.32). Pheochromocytoma had higher T2W-SI-ratio (p < 0.001) and entropy (p < 0.001) for all three readers. The quantitative calculator differentiated pheochromocytoma from adenoma with high sensitivity, specificity, and accuracy (100% [95% confidence intervals 88-100%], 87% [66-97%], and 94% [86-100%] R1; 93% [77-99%], 96% [78-100%], and 94% [88-100%] R2; 97% [82-100%], 96% [78-100%], and 96% [91-100% R3]). The qualitative calculator was specific with lower sensitivity and overall accuracy (48% [29-68%], 100% [85-100%], and 74% [65-83%] R1; 45% [26-64%], 100% [85-100%], and 72% [63-82%] R2; 59% [39-77%], 100% [85-100%], and 79% [70-88% R3]).

CONCLUSIONS

T2W signal intensity and heterogeneity differ, subjectively and quantitatively, in pheochromocytoma compared to adenoma. Use of a quantitative T2W adrenal calculator which combines T2W signal intensity ratio and entropy was highly accurate to diagnose pheochromocytoma outperforming subjective analysis.

KEY POINTS

• Pheochromocytomas have higher T2-weighted signal intensity and are more heterogeneous compared to lipid-poor adrenal adenomas evaluated subjectively and quantitatively. • The quantitative T2-weighted adrenal MRI calculator, a logistic regression model combining T2-weighted signal intensity ratio and entropy, is highly accurate for diagnosis of pheochromocytoma. • The qualitative T2-weighed adrenal MRI calculator had high specificity but lower sensitivity and overall accuracy compared to quantitative assessment and agreement was only fair-to-moderate.

Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study

In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = −5–8) and 6% (IQR = 0–22%), respectively. The highest and lowest scores registered were 12/36 (33%) and −5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.