Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma

Ahmed, A.A.; Elmohr, Mohab M.; Fuentes, David; Habra, Mouhammed Amir; Fisher, Sarah B.; Perrier, Nancy D.; Zhang, Miao; Elsayes, Khaled M.

doi:10.1016/j.crad.2020.01.012

Cited by 22 publications

(22 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model demonstrated an accuracy of 82%, compared with 69% for board-trained radiologists with 10 and 15 years’ experience. Comparable positive results were found in other similar studies differentiating adrenocortical adenoma from carcinoma [56] and predicting the Ki-67 expression in adrenal masses [57].…”

Section: Radiomics and Artificial Intelligencesupporting

confidence: 81%

Optimal and novel imaging of the adrenal glands

Navin¹,

Moynagh²

2022

Current Opinion in Endocrinology, Diabetes &Amp; Obesity

View full text Add to dashboard Cite

Purpose of reviewAdrenal imaging forms an important role in the workup of adrenal masses. The purpose of this review is to briefly review the traditional role of imaging in adrenal diseases and highlight the most recent research and new applications aimed to improve diagnostic accuracy. Recent findingsThe current review will focus on new applications of computed tomography (CT), MRI and PET/CT imaging, addressing the implications of artificial intelligence and radiomics in progressing diagnostic accuracy.

show abstract

Section: Radiomics and Artificial Intelligencesupporting

confidence: 81%

Optimal and novel imaging of the adrenal glands

Navin¹,

Moynagh²

2022

Current Opinion in Endocrinology, Diabetes &Amp; Obesity

View full text Add to dashboard Cite

show abstract

“…In total, five studies were found which investigated the correlation between radiomics and molecular markers in other entities not included in the sections above: melanoma ( n = 1) [ 117 ], thyroid cancer ( n = 1) [ 118 ], head and neck cancer ( n = 2) [ 119 , 120 ], adrenal gland carcinoma ( n = 1) [ 121 ]. All studies showed a significant correlation between the biomarker and radiomics (AUC = 0.62–0.78).…”

Section: Resultsmentioning

confidence: 99%

“…The GLCM-derived feature of correlation was found to be a negative predictor of PD-L1 expression, while it was positively associated with VEGF expression. One study investigated the efficacy of CE CT radiomics to predict Ki-67 expression in adrenal gland carcinoma patients [ 121 ]. The authors reported final AUCs of 0.7–0.78 on the training cohort after using logistic regression models based on two shape features, suggesting that high Ki-67 expression is associated with flatter and more elongated tumors.…”

Section: Resultsmentioning

confidence: 99%

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Saint-Esteven

Vuong

Tschanz

et al. 2021

Cancers

View full text Add to dashboard Cite

Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.

show abstract

“…Yi et al defined a radiomic signature for preoperative differentiation between subclinical pheochromocytoma and lipid-poor adrenal adenoma using the largest cross-sectional area of the tumor 27 . A.A. Ahmed et al used 3D radiomic features in preoperative CT studies to predict the Ki-67 index in patients with adrenocortical carcinoma 28 . While it is more convenient to use 2D images in clinical applications for radiomic analysis, lesion heterogeneity cannot be expressed in only a single cross-sectional image.…”

Section: Discussionmentioning

confidence: 99%

Radiomics utilization to differentiate nonfunctional adenoma in essential hypertension and functional adenoma in primary aldosteronism

Chen

Chang

Liu

et al. 2022

Sci Rep

View full text Add to dashboard Cite

We performed the present study to investigate the role of computed tomography (CT) radiomics in differentiating nonfunctional adenoma and aldosterone-producing adenoma (APA) and outcome prediction in patients with clinically suspected primary aldosteronism (PA). This study included 60 patients diagnosed with essential hypertension (EH) with nonfunctional adenoma on CT and 91 patients with unilateral surgically proven APA. Each whole nodule on unenhanced and venous phase CT images was segmented manually and randomly split into training and test sets at a ratio of 8:2. Radiomic models for nodule discrimination and outcome prediction of APA after adrenalectomy were established separately using the training set by least absolute shrinkage and selection operator (LASSO) logistic regression, and the performance was evaluated on test sets. The model can differentiate adrenal nodules in EH and PA with a sensitivity, specificity, and accuracy of 83.3%, 78.9% and 80.6% (AUC = 0.91 [0.72, 0.97]) in unenhanced CT and 81.2%, 100% and 87.5% (AUC = 0.98 [0.77, 1.00]) in venous phase CT, respectively. In the outcome after adrenalectomy, the models showed a favorable ability to predict biochemical success (Unenhanced/venous CT: AUC = 0.67 [0.52, 0.79]/0.62 [0.46, 0.76]) and clinical success (Unenhanced/venous CT: AUC = 0.59 [0.47, 0.70]/0.64 [0.51, 0.74]). The results showed that CT-based radiomic models hold promise to discriminate APA and nonfunctional adenoma when an adrenal incidentaloma was detected on CT images of hypertensive patients in clinical practice, while the role of radiomic analysis in outcome prediction after adrenalectomy needs further investigation.

show abstract

Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma

Cited by 22 publications

References 28 publications

Optimal and novel imaging of the adrenal glands

Optimal and novel imaging of the adrenal glands

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Radiomics utilization to differentiate nonfunctional adenoma in essential hypertension and functional adenoma in primary aldosteronism

Contact Info

Product

Resources

About