Using CT radiomic features based on machine learning models to subtype adrenal adenoma

Qi, Shouliang; Zuo, Yifan; Chang, Runsheng; Huang, Kun; Liu, Jing; Zhang, Zhe

doi:10.1186/s12885-023-10562-6

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Radiomic features can re ect tumor heterogeneity and show great potential in distinguishing different types of tumors. Radiomics for medical image analysis identify diagnostic image biomarkers which can help clinicians optimize the preoperative diagnosis of LPA and sPHEO 22 . The main purpose of this study was to identify LPA and sPHEO based on CT radiomics and evaluate the effectiveness of distinguishing LPA and sPHEO based on different phases and slice thickness of radiomic features to obtain the simplest method and optimal model to improve the preoperative diagnostic accuracy of both.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

Xiao

Peng

Fan

et al. 2023

Preprint

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Objective The aim of this study was to use radiomics analysis of multiphase computed tomography (CT) imaging to develop and validate machine learning models that can accurately differentiate between lipid-poor adrenal adenoma (LPA) and subclinical pheochromocytoma (sPHEO) to improve the accuracy of preoperative diagnosis of the two.Methods A retrospective analysis was performed on 134 patients who underwent abdominal multiphase spiral CT scans in three local tertiary hospitals between March 2015 and November 2022. The analysis included 74 cases of LPA (52 at our hospital and 22 at other hospitals) and 60 cases of sPHEO (44 at our hospital and 16 at other hospitals), all of which were surgically and pathologically confirmed. Tumors were delineated using 3D Slicer and radiomics were extracted using PyRadiomics, a plug-in to the software. Cases from internal hospital were randomly split into training and test sets in a 7:3 ratio, while all cases from external hospitals were used as the validation set. The T-test and the minimum absolute contraction and selection operator (LASSO) were used to reduce the dimensionality of the data. Then six dichotomous models were developed, including k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP). The diagnostic performance of each model was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The ROC curves of the test and validation sets were compared using DeLong's method to determine the most effective model for distinguishing between LPA and sPHEO.Results A total of 902 rows and 112 columns of radiomic feature data were extracted from multiple phases and slice-thickness CT data. After dimensionality reduction processing, 13 - dimensional radiomic feature data was obtained. The six binary models demonstrated good diagnostic performance for each phase and slice thickness, as well as for the entire CT data, with AUC values ranging from 0.706 to 1. Among these models, RF, SVM, and MLP showed particularly good diagnostic performance. The ROC curves of RF, SVM, and MLP did not show a statistically significant difference (p < 0.05) for different phase, slice-thicknesses, as well as the entire test and validation sets, except for the thick slice-thickness data sets. The AUC value of the MLP model for the non-contrast CT validation set was 0.979, which is quite high. Furthermore, there was no significant difference in the ROC curves when compared to other phases and the entire validation sets (p < 0.05).Conclusions The CT radiomics-based machine learning model was able to differentiate between LPA and sPHEO well, even using non-contrast CT data alone to efficiently discriminate between the two.

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Section: Discussionmentioning

confidence: 99%

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

Xiao

Peng

Fan

et al. 2023

Preprint

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Artificial intelligence in COPD CT images: identification, staging, and quantitation

Wu,

Xia,

Liang

et al. 2024

Respir Res

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Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn’t just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.

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Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation?

Toniolo,

Agostini,

Ceccato

et al. 2024

Current Oncology

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We studied the application of CT texture analysis in adrenal incidentalomas with baseline characteristics of benignity that are highly suggestive of adenoma to find whether there is a correlation between the extracted features and clinical data. Patients with hormonal hypersecretion may require medical attention, even if it does not cause any symptoms. A total of 206 patients affected by adrenal incidentaloma were retrospectively enrolled and divided into non-functioning adrenal adenomas (NFAIs, n = 115) and mild autonomous cortisol secretion (MACS, n = 91). A total of 136 texture parameters were extracted in the unenhanced phase for each volume of interest (VOI). Random Forest was used in the training and validation cohorts to test the accuracy of CT textural features and cortisol-related comorbidities in identifying MACS patients. Twelve parameters were retained in the Random Forest radiomic model, and in the validation cohort, a high specificity (81%) and positive predictive value (74%) were achieved. Notably, if the clinical data were added to the model, the results did not differ. Radiomic analysis of adrenal incidentalomas, in unenhanced CT scans, could screen with a good specificity those patients who will need a further endocrinological evaluation for mild autonomous cortisol secretion, regardless of the clinical information about the cortisol-related comorbidities.

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Using CT radiomic features based on machine learning models to subtype adrenal adenoma

Cited by 4 publications

References 45 publications

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

Artificial intelligence in COPD CT images: identification, staging, and quantitation

Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation?

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