E. Serrano , J. Moreno , L. Llull , A. Rodríguez , C. Zwanzger , S. Amaro , L. Oleaga , A. López-Rueda
{"title":"基于无对比脑ct放射学的非线性监督学习分类器预测自发性脑内血肿患者的功能预后","authors":"E. Serrano , J. Moreno , L. Llull , A. Rodríguez , C. Zwanzger , S. Amaro , L. Oleaga , A. López-Rueda","doi":"10.1016/j.rx.2023.08.001","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>To evaluate if nonlinear supervised learning classifiers based on non-contrast cerebral CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma (HIE).</p></div><div><h3>Methods</h3><p>Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE ><!--> <!-->18<!--> <!-->years and with non-contrast CT performed within the first 24<!--> <!-->hours of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS<!--> <!-->0-2) and poor prognosis (mRS<!--> <!-->3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30%, respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort.</p></div><div><h3>Results</h3><p>105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC: 0.798, 0.752 and 0.742, respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (95%<!--> <!-->CI: 0.778-1), with a false-negative rate of 0% for predicting poor functional prognosis at discharge.</p></div><div><h3>Conclusion</h3><p>The use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.</p></div>","PeriodicalId":31509,"journal":{"name":"RADIOLOGIA","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clasificadores de aprendizaje supervisado no lineales basados en radiómica de la TC cerebral sin contraste para predecir el pronóstico funcional en pacientes con hematoma intracerebral espontáneo\",\"authors\":\"E. Serrano , J. Moreno , L. Llull , A. Rodríguez , C. Zwanzger , S. Amaro , L. Oleaga , A. López-Rueda\",\"doi\":\"10.1016/j.rx.2023.08.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>To evaluate if nonlinear supervised learning classifiers based on non-contrast cerebral CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma (HIE).</p></div><div><h3>Methods</h3><p>Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE ><!--> <!-->18<!--> <!-->years and with non-contrast CT performed within the first 24<!--> <!-->hours of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS<!--> <!-->0-2) and poor prognosis (mRS<!--> <!-->3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30%, respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort.</p></div><div><h3>Results</h3><p>105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC: 0.798, 0.752 and 0.742, respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (95%<!--> <!-->CI: 0.778-1), with a false-negative rate of 0% for predicting poor functional prognosis at discharge.</p></div><div><h3>Conclusion</h3><p>The use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.</p></div>\",\"PeriodicalId\":31509,\"journal\":{\"name\":\"RADIOLOGIA\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"RADIOLOGIA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0033833823001637\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"RADIOLOGIA","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0033833823001637","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Clasificadores de aprendizaje supervisado no lineales basados en radiómica de la TC cerebral sin contraste para predecir el pronóstico funcional en pacientes con hematoma intracerebral espontáneo
Purpose
To evaluate if nonlinear supervised learning classifiers based on non-contrast cerebral CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma (HIE).
Methods
Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE > 18 years and with non-contrast CT performed within the first 24 hours of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS 0-2) and poor prognosis (mRS 3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30%, respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort.
Results
105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC: 0.798, 0.752 and 0.742, respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (95% CI: 0.778-1), with a false-negative rate of 0% for predicting poor functional prognosis at discharge.
Conclusion
The use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.
RADIOLOGIARADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
1.60
自引率
7.70%
发文量
105
审稿时长
52 days
期刊介绍:
La mejor revista para conocer de primera mano los originales más relevantes en la especialidad y las revisiones, casos y notas clínicas de mayor interés profesional. Además es la Publicación Oficial de la Sociedad Española de Radiología Médica.