Machine learning models for differential diagnosis of Cushing’s disease and ectopic ACTH secretion syndrome

Lyu, Xiaohong; Zhang, Dingyue; Pan, Hui; Zhu, Huijuan; Chen, Shi; Lü, Lin

doi:10.1007/s12020-023-03341-7

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Впервые исследования возможности методов машинного обучения в области дифференциальной диагностики АКТГ-зависимых форм эндогенного гиперкортицизма (БИК и АКТГ-ЭС) проведены китайскими учеными Lyu X. и соавт. [ 13 ][ 14 ]. Данные 311 пациентов, включая 47 больных с АКТГ-ЭС, были использованы для моделирования.…”

Section: Discussionunclassified

“…По результатам исследования, тремя наиболее значимыми переменными оказались уровень калия в сыворотке крови, размер аденомы гипофиза на МРТ и значения АКТГ. Модель, основанная на алгоритме RF, имела наибольшую площадь под ROC-кривой (AUC 0,984 (95% ДИ 0,950–0,993)) в сравнении с AUC для МДП и БДП (p<0,001), однако не выявлено статистически значимых различий при сравнении площадей выбранной модели и метода «золотого стандарта» — двустороннего селективного забора крови из нижних каменистых синусов с использованием стимуляционного агента [ 14 ].…”

Section: Discussionunclassified

“…Результаты обследований пациентки О. [14]. В отличие от вышеупомянутого исследования, в нашей работе к универсальным предикторам, определяющим высокую точность прогнозирования, помимо уровня АКТГ в ранние утренние часы и калия, относились такие показатели, как концентрация свободного кортизола в суточной моче, кортизола в крови в 23:00, кортизола в образце слюны, собранной в 23:00, и наибольший диаметр аденомы гипофиза по результатам МРТ головного мозга.…”

Section: Discussionunclassified

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Machine learning methods in differential diagnosis of ACTH-dependent hypercortisolism

Golounina,

Belaya,

Voronov

et al. 2024

Probl Endokrinol (Mosk)

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AIM: To develop a noninvasive method of differential diagnosis of ACTH-dependent hypercortisolism, as well as to evaluate the effectiveness of an optimal algorithm for predicting the probability of ectopic ACTH syndrome (EAS) obtained using machine learning methods based on the analysis of clinical data.MATERIALS AND METHODS: As part of a single-center, one-stage, cohort study, a retrospective prediction of the probability of EAS among patients with ACTH-dependent hypercortisolism was carried out. Patients were randomly stratified into 2 samples: training (80%) and test (20%). Eleven machine learning algorithms were used to develop predictive models: Linear Discriminant Analysis, Logistic Regression, elastic network (GLMNET), Support Vector machine (SVM Radial), k-nearest neighbors (kNN), Naive Bayes, binary decision tree (CART), C5.0 decision tree algorithms, Bagged CART, Random Forest, Gradient Boosting (Stochastic Gradient Boosting, GBM).RESULTS: The study included 223 patients (163 women, 60 men) with ACTH-dependent hypercortisolism, of which 175 patients with Cushing’s disease (CD), 48 — with EAS. As a result of preliminary data processing and selection of the most informative signs, the final variables for the classification and prediction of EAS were selected: ACTH level at 08:00 hours, potassium level (the minimum value of potassium in the active stage of the disease), 24-h urinary free cortisol, late-night serum cortisol, late-night salivary cortisol, the largest size of pituitary adenoma according to MRI of the brain. The best predictive ability in a training sample of all trained machine learning models for all three final metrics (ROC-AUC (0.867), sensitivity (90%), specificity (56.4%)) demonstrated a model of gradient boosting (Generalized Boosted Modeling, GBM). In the test sample, the AUC, sensitivity and specificity of the model in predicting EAS were 0.920; 77.8% and 97.1%, respectively.CONCLUSION: The prognostic model based on machine learning methods makes it possible to differentiate patients with EAS and CD based on basic clinical results and can be used as a primary screening of patients with ACTH-dependent hypercortisolism.

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Section: Discussionunclassified

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Machine learning methods in differential diagnosis of ACTH-dependent hypercortisolism

Golounina,

Belaya,

Voronov

et al. 2024

Probl Endokrinol (Mosk)

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Cushing's Syndrome in the Elderly

Zdrojowy-Wełna,

Valassi

2024

Exp Clin Endocrinol Diabetes

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Management of Cushing’s syndrome (CS) can be particularly challenging in older patients, due to the lack of several clinical features associated with cortisol excess along with a greater burden of comorbidities in them, as compared with the younger. Moreover, interpretation of diagnostic tests could be influenced by age-related physiological changes of cortisol secretion. While mortality is higher and quality of life is more impaired in the elderly with CS as compared with the younger, there is currently no agreement on the most effective therapeutic options in aged individuals, and safety data concerning medical treatment are scanty. In this review, we summarize the current knowledge about age-related differences in etiology, clinical presentation, treatment and outcomes of CS, and describe the potential underlying mechanisms.

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Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses

Tenhoeve,

Lefler,

Brown

et al. 2024

J Neurol Surg B Skull Base

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Objectives Radiomics involves the extraction and analysis of numerous quantitative features of medical imaging which can add more information from radiological images often beyond initial comprehension of a clinician. Unlike deep learning, radiomics allows some understanding of identified quantitative features for clinical prediction. We sought to explore the current state of radiomics applications in the skull base literature. Methods A systematic review of studies evaluating radiomics in skull base was performed, including those with and without machine-learning approaches. Studies were summarized into thematic elements as well as specific pathologies. Results A total of 102 studies with 26,280 radiographic images were included. The earliest radiomic study was published in 2017 with exponential growth in research since then. Most studies focused on tumor diagnosis (40.8%), followed by tumor prognosis (31.1%), automated segmentation (16.5%), other applications (7.8%), and lastly prediction of intraoperative features (3.9%). Pituitary adenomas (41.7%) and vestibular schwannomas (18.4%) represented the most commonly evaluated pathologies; however, radiomics could be applied to a heterogeneous collection of skull base pathologies. The average study included 258677 cases (range 4; 6755). Conclusion Radiomics offers many functions in treating skull base pathology and will likely be an essential component of future clinical care. Larger sample sizes, validation of predictive models, and clinical application are needed. Further investigation into the strengths and weaknesses of radiomic applications in skull base treatments is warranted.

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Machine learning models for differential diagnosis of Cushing’s disease and ectopic ACTH secretion syndrome

Cited by 5 publications

References 25 publications

Machine learning methods in differential diagnosis of ACTH-dependent hypercortisolism

Machine learning methods in differential diagnosis of ACTH-dependent hypercortisolism

Cushing's Syndrome in the Elderly

Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses

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