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PURPOSE: We aimed to investigate the impact of post-thrombectomy isolated subarachnoid hemorrhage (i-SAH) and other types of intracranial hemorrhage (o-ICH) on patient's neurological outcomes. METHODS: Stroke data from 2018 to 2022 in a tertiary care center were retrospectively analyzed. Patients with large vessel occlusion from ICA to M2 branch were included. Post-thrombectomy intracranial hemorrhages at 24 h were categorized with Heidelberg Bleeding Classification. Neurological impairment of patients was continuously assessed at admission, at 24 h, 48 h and 72 h, and at discharge. Predictors of i-SAH and o-ICH were assessed. RESULTS: 297 patients were included. i-SAH and o-ICH were found in 12.1% (36/297) and 11.4% (34/297) of patients. Overall, NIHSS of i-SAH patients at discharge were comparable to o-ICH patients (median 22 vs. 21, p = 0.889) and were significantly higher than in non-ICH patients (22 vs. 7, p < 0.001). i-SAH often resulted in abrupt deterioration of patient's neurological symptoms at 24 h after thrombectomy. Compared to non-ICH patients, the occurrence of i-SAH was frequently associated with worse neurological outcome at discharge (median NIHSS increase of 4 vs. decrease of 4, p < 0.001) and higher in-hospital mortality (41.7% vs. 23.8%, p = 0.022). Regardless of successful reperfusion (TICI 2b/3), the beneficial impact of thrombectomy appeared to be outweighed by the adverse effect of i-SAH. Incomplete reperfusion and shorter time from symptom onset to admission were associated with higher probability of i-SAH, whereas longer procedure time and lower baseline ASPECTS were predictive for o-ICH occurrence. CONCLUSION: Post-thrombectomy isolated subarachnoid hemorrhage is a common complication with significant negative impact on neurological outcome.
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Class imbalance is often unavoidable for radiomic data collected from clinical routine. It can create problems during classifier training since the majority class could dominate the minority class. Consequently, resampling methods like oversampling or undersampling are applied to the data to class-balance the data. However, the resampling must not be applied upfront to all data because it would lead to data leakage and, therefore, to erroneous results. This study aims to measure the extent of this bias. Five-fold cross-validation with 30 repeats was performed using a set of 15 radiomic datasets to train predictive models. The training involved two scenarios: first, the models were trained correctly by applying the resampling methods during the cross-validation. Second, the models were trained incorrectly by performing the resampling on all the data before cross-validation. The bias was defined empirically as the difference between the best-performing models in both scenarios in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity, balanced accuracy, and the Brier score. In addition, a simulation study was performed on a randomly generated dataset for verification. The results demonstrated that incorrectly applying the oversampling methods to all data resulted in a large positive bias (up to 0.34 in AUC, 0.33 in sensitivity, 0.31 in specificity, and 0.37 in balanced accuracy). The bias depended on the data balance, and approximately an increase of 0.10 in the AUC was observed for each increase in imbalance. The models also showed a bias in calibration measured using the Brier score, which differed by up to -0.18 between the correctly and incorrectly trained models. The undersampling methods were not affected significantly by bias. These results emphasize that any resampling method should be applied correctly only to the training data to avoid data leakage and, subsequently, biased model performance and calibration.
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Viés , Humanos , Curva ROC , Área Sob a Curva , RadiômicaRESUMO
In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.
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Aprendizado Profundo , Cabeça , Software , Tomografia Computadorizada por Raios X , Humanos , Feminino , Tomografia Computadorizada por Raios X/métodos , Masculino , Idoso , Cabeça/diagnóstico por imagem , Estudos Retrospectivos , Pessoa de Meia-Idade , Idoso de 80 Anos ou mais , Processamento de Imagem Assistida por Computador/métodos , Razão Sinal-Ruído , Adulto , Algoritmos , Interpretação de Imagem Radiográfica Assistida por Computador/métodosRESUMO
BACKGROUND: Data on impact of COVID-19 vaccination and outcomes of patients with COVID-19 and acute ischemic stroke undergoing mechanical thrombectomy are scarce. Addressing this subject, we report our multicenter experience. METHODS AND RESULTS: This was a retrospective analysis of patients with COVID-19 and known vaccination status treated with mechanical thrombectomy for acute ischemic stroke at 20 tertiary care centers between January 2020 and January 2023. Baseline demographics, angiographic outcome, and clinical outcome evaluated by the modified Rankin Scale score at discharge were noted. A multivariate analysis was conducted to test whether these variables were associated with an unfavorable outcome, defined as modified Rankin Scale score >3. A total of 137 patients with acute ischemic stroke (48 vaccinated and 89 unvaccinated) with acute or subsided COVID-19 infection who underwent mechanical thrombectomy attributable to vessel occlusion were included in the study. Angiographic outcomes between vaccinated and unvaccinated patients were similar (modified Thrombolysis in Cerebral Infarction ≥2b: 85.4% in vaccinated patients versus 86.5% in unvaccinated patients; P=0.859). The rate of functional independence (modified Rankin Scale score, ≤2) was 23.3% in the vaccinated group and 20.9% in the unvaccinated group (P=0.763). The mortality rate was 30% in both groups. In the multivariable analysis, vaccination status was not a significant predictor for an unfavorable outcome (P=0.957). However, acute COVID-19 infection remained significant (odds ratio, 1.197 [95% CI, 1.007-1.417]; P=0.041). CONCLUSIONS: Our study demonstrated no impact of COVID-19 vaccination on angiographic or clinical outcome of COVID-19-positive patients with acute ischemic stroke undergoing mechanical thrombectomy, whereas worsening attributable to COVID-19 was confirmed.
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Vacinas contra COVID-19 , COVID-19 , AVC Isquêmico , Trombectomia , Vacinação , Humanos , COVID-19/complicações , COVID-19/terapia , COVID-19/mortalidade , Masculino , Feminino , AVC Isquêmico/mortalidade , AVC Isquêmico/cirurgia , Estudos Retrospectivos , Idoso , Pessoa de Meia-Idade , Resultado do Tratamento , Vacinas contra COVID-19/efeitos adversos , SARS-CoV-2 , Idoso de 80 Anos ou maisRESUMO
Non-contrast computed tomography (CT) is commonly used for the evaluation of various pathologies including pulmonary infections or urolithiasis but, especially in low-dose protocols, image quality is reduced. To improve this, deep learning-based post-processing approaches are being developed. Therefore, we aimed to compare the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast chest and low-dose abdominal CTs. In this retrospective study, non-contrast chest CTs of patients suspected of COVID-19 pneumonia and low-dose abdominal CTs suspected of urolithiasis were analysed. All images were reconstructed using filtered back-projection (FBP) and were post-processed using an artificial intelligence (AI)-based commercial software (PixelShine (PS)). Additional iterative reconstruction (IR) was performed for abdominal CTs. Objective and subjective image quality were evaluated. AI-based post-processing led to an overall significant noise reduction independent of the protocol (chest or abdomen) while maintaining image information (max. difference in SNR 2.59 ± 2.9 and CNR 15.92 ± 8.9, p < 0.001). Post-processing of FBP-reconstructed abdominal images was even superior to IR alone (max. difference in SNR 0.76 ± 0.5, p ≤ 0.001). Subjective assessments verified these results, partly suggesting benefits, especially in soft-tissue imaging (p < 0.001). All in all, the deep learning-based denoising-which was non-inferior to IR-offers an opportunity for image quality improvement especially in institutions using older scanners without IR availability. Further studies are necessary to evaluate potential effects on dose reduction benefits.
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PURPOSE: The aim of this study was to investigate the potential of multiparametric 18F-FDG PET/MR imaging as a platform for radiomics analysis and machine learning algorithms based on primary cervical cancers to predict N- and M-stage in patients. MATERIALS AND METHODS: A total of 30 patients with histopathological confirmation of primary and untreated cervical cancer were prospectively enrolled for a multiparametric 18F-FDG PET/MR examination, comprising a dedicated protocol for imaging of the female pelvis. The primary tumor in the uterine cervix was manually segmented on post-contrast T1-weighted images. Quantitative features were extracted from the segmented tumors using the Radiomic Image Processing Toolbox for the R software environment for statistical computing and graphics. 45 different image features were calculated from non-enhanced as well as post-contrast T1-weighted TSE images, T2-weighted TSE images, the ADC map, the parametric Ktrans, Kep, Ve and iAUC maps and PET images, respectively. Statistical analysis and modeling was performed using Python 3.5 and the scikit-learn software machine learning library for the Python programming language. RESULTS: Prediction of M-stage was superior when compared to N-stage. Prediction of M-stage using SVM with SVM-RFE as feature selection obtained the highest performance providing sensitivity of 91â% and specificity of 92â%. Using receiver operating characteristic (ROC) analysis of the pooled predictions, the area under the curve (AUC) was 0.97. Prediction of N-stage using RBF-SVM with MIFS as feature selection reached sensitivity of 83â%, specificity of 67â% and an AUC of 0.82. CONCLUSION: M- and N-stage can be predicted based on isolated radiomics analyses of the primary tumor in cervical cancers, thus serving as a template for noninvasive tumor phenotyping and patient stratification using high-dimensional feature vectors extracted from multiparametric PET/MRI data. KEY POINTS: · Radiomics analysis based on multiparametric PET/MRI enables prediction of the metastatic status of cervical cancers. · Prediction of M-stage is superior to N-stage. · Multiparametric PET/MRI displays a valuable platform for radiomics analyses .
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Neoplasias do Colo do Útero , Humanos , Feminino , Neoplasias do Colo do Útero/diagnóstico por imagem , Fluordesoxiglucose F18 , Radiômica , Estudos Retrospectivos , Imageamento por Ressonância MagnéticaRESUMO
Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).
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Análise de Dados , Radiômica , Conjuntos de Dados como AssuntoRESUMO
PURPOSE: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS: ⢠A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. ⢠The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. ⢠METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. ⢠A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).
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OBJECTIVES: In radiomics, different feature normalization methods, such as z-Score or Min-Max, are currently utilized, but their specific impact on the model is unclear. We aimed to measure their effect on the predictive performance and the feature selection. METHODS: We employed fifteen publicly available radiomics datasets to compare seven normalization methods. Using four feature selection and classifier methods, we used cross-validation to measure the area under the curve (AUC) of the resulting models, the agreement of selected features, and the model calibration. In addition, we assessed whether normalization before cross-validation introduces bias. RESULTS: On average, the difference between the normalization methods was relatively small, with a gain of at most + 0.012 in AUC when comparing the z-Score (mean AUC: 0.707 ± 0.102) to no normalization (mean AUC: 0.719 ± 0.107). However, on some datasets, the difference reached + 0.051. The z-Score performed best, while the tanh transformation showed the worst performance and even decreased the overall predictive performance. While quantile transformation performed, on average, slightly worse than the z-Score, it outperformed all other methods on one out of three datasets. The agreement between the features selected by different normalization methods was only mild, reaching at most 62%. Applying the normalization before cross-validation did not introduce significant bias. CONCLUSION: The choice of the feature normalization method influenced the predictive performance but depended strongly on the dataset. It strongly impacted the set of selected features. CRITICAL RELEVANCE STATEMENT: Feature normalization plays a crucial role in the preprocessing and influences the predictive performance and the selected features, complicating feature interpretation. KEY POINTS: ⢠The impact of feature normalization methods on radiomic models was measured. ⢠Normalization methods performed similarly on average, but differed more strongly on some datasets. ⢠Different methods led to different sets of selected features, impeding feature interpretation. ⢠Model calibration was not largely affected by the normalization method.
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OBJECTIVES: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep neural network to optimize the delimitation of scan ranges in CT localizers to reduce the radiation dose. METHODS: On a retrospective training cohort of 1507 CT localizers randomly selected from calcium scoring and angiography scans and acquired between 2010 and 2017, optimized scan ranges were delimited by two radiologists in consensus. A neural network was trained to reproduce the scan ranges and was tested on two randomly selected and independent validation cohorts: an internal cohort of 233 CT localizers (January 2018-June 2020) and an external cohort from a nearby hospital of 298 CT localizers (July 2020-December 2020). Localizers where a bypass surgery was visible were excluded. The effective radiation dose to the patient was simulated using a Monte Carlo simulation. Scan ranges of radiographers, radiologists, and the network were compared using an equivalence test; likewise, the reduction in effective dose was tested using a superior test. RESULTS: The network replicated the radiologists' scan ranges with a Dice score of 96.5 ± 0.02 (p < 0.001, indicating equivalence). The generated scan ranges resulted in an effective dose reduction of 10.0% (p = 0.002) in the internal cohort and 12.6% (p < 0.001) in the external cohort compared to the scan ranges delimited by radiographers in clinical routine. CONCLUSIONS: Automatic delimitation of the scan range can result in a radiation dose reduction to the patient. CLINICAL RELEVANCE STATEMENT: Fully automated delimitation of the scan range using a deep neural network enables a significant reduction in radiation exposure during CT coronary angiography compared to manual examination planning. It can also reduce the workload of the radiographers. KEY POINTS: ⢠Scan range delimitation for coronary computed tomography angiography could be performed with high accuracy by a deep neural network. ⢠Automated scan ranges showed a high agreement of 96.5% with the scan ranges of radiologists. ⢠Using a Monte Carlo simulation, automated scan ranges reduced the effective dose to the patient by up to 12.6% (0.9 mSv) compared to the scan ranges of radiographers in clinical routine.
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Aprendizado Profundo , Exposição à Radiação , Humanos , Angiografia Coronária/métodos , Angiografia por Tomografia Computadorizada/métodos , Doses de Radiação , Estudos Retrospectivos , Exposição à Radiação/prevenção & controleRESUMO
In this retrospective study, we aimed to predict the body height and weight of pediatric patients using CT localizers, which are overview scans performed before the acquisition of the CT. We trained three commonly used networks (EfficientNetV2-S, ResNet-18, and ResNet-34) on a cohort of 1009 and 1111 CT localizers of pediatric patients with recorded body height and weight (between January 2013 and December 2019) and validated them in an additional cohort of 116 and 127 localizers (acquired in 2020). The best-performing model was then tested in an independent cohort of 203 and 225 CT localizers (acquired between January 2021 and March 2023). In addition, a cohort of 1401 and 1590 localizers from younger adults (acquired between January 2013 and December 2013) was added to the training set to determine if it could improve the overall accuracy. The EfficientNetV2-S using the additional adult cohort performed best with a mean absolute error of 5.58 ± 4.26 cm for height and 4.25 ± 4.28 kg for weight. The relative error was 4.12 ± 4.05% for height and 11.28 ± 12.05% for weight. Our study demonstrated that automated estimation of height and weight in pediatric patients from CT localizers can be performed.
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Aprendizado Profundo , Humanos , Criança , Adulto Jovem , Estudos Retrospectivos , Estatura , Tomografia Computadorizada por Raios XRESUMO
In radiomics, utilizing features extracted from pretrained deep networks could result in models with a higher predictive performance than those relying on hand-crafted features. This study compared the predictive performance of models trained with either deep features, hand-crafted features, or a combination of these features in terms of the area under the receiver-operating characteristic curve (AUC) and other metrics. We trained models on ten radiological datasets using five feature selection methods and three classifiers. Our results indicate that models based on deep features did not show an improved AUC compared to those utilizing hand-crafted features (deep: AUC 0.775, hand-crafted: AUC 0.789; p = 0.28). Including morphological features alongside deep features led to overall improvements in prediction performance for all models (+0.02 gain in AUC; p < 0.001); however, the best model did not benefit from this (+0.003 gain in AUC; p = 0.57). Using all hand-crafted features in addition to the deep features resulted in a further overall improvement (+0.034 in AUC; p < 0.001), but only a minor improvement could be observed for the best model (deep: AUC 0.798, hand-crafted: AUC 0.789; p = 0.92). Furthermore, our results show that models based on deep features extracted from networks pretrained on medical data have no advantage in predictive performance over models relying on features extracted from networks pretrained on ImageNet data. Our study contributes a benchmarking analysis of models trained on hand-crafted and deep features from pretrained networks across multiple datasets. It also provides a comprehensive understanding of their applicability and limitations in radiomics. Our study shows, in conclusion, that models based on features extracted from pretrained deep networks do not outperform models trained on hand-crafted ones.
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BACKGROUND: Application of radiomics proceeds by extracting and analysing imaging features based on generic morphological, textural, and statistical features defined by formulas. Recently, deep learning methods were applied. It is unclear whether deep models (DMs) can outperform generic models (GMs). METHODS: We identified publications on PubMed and Embase to determine differences between DMs and GMs in terms of receiver operating area under the curve (AUC). RESULTS: Of 1,229 records (between 2017 and 2021), 69 studies were included, 61 (88%) on tumours, 68 (99%) retrospective, and 39 (56%) single centre; 30 (43%) used an internal validation cohort; and 18 (26%) applied cross-validation. Studies with independent internal cohort had a median training sample of 196 (range 41-1,455); those with cross-validation had only 133 (43-1,426). Median size of validation cohorts was 73 (18-535) for internal and 94 (18-388) for external. Considering the internal validation, in 74% (49/66), the DMs performed better than the GMs, vice versa in 20% (13/66); no difference in 6% (4/66); and median difference in AUC 0.045. On the external validation, DMs were better in 65% (13/20), GMs in 20% (4/20) cases; no difference in 3 (15%); and median difference in AUC 0.025. On internal validation, fused models outperformed GMs and DMs in 72% (20/28), while they were worse in 14% (4/28) and equal in 14% (4/28); median gain in AUC was + 0.02. On external validation, fused model performed better in 63% (5/8), worse in 25% (2/8), and equal in 13% (1/8); median gain in AUC was + 0.025. CONCLUSIONS: Overall, DMs outperformed GMs but in 26% of the studies, DMs did not outperform GMs.
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Interpretação de Imagem Radiográfica Assistida por Computador , Radiometria , Humanos , Modelos TeóricosRESUMO
Age assessment is regularly used in clinical routine by pediatric endocrinologists to determine the physical development or maturity of children and adolescents. Our study investigates whether age assessment can be performed using CT scout views from thoracic and abdominal CT scans using a deep neural network. Hence, we retrospectively collected 1949 CT scout views from pediatric patients (acquired between January 2013 and December 2018) to train a deep neural network to predict the chronological age from CT scout views. The network was then evaluated on an independent test set of 502 CT scout views (acquired between January 2019 and July 2020). The trained model showed a mean absolute error of 1.18 ± 1.14 years on the test data set. A one-sided t-test to determine whether the difference between the predicted and actual chronological age was less than 2.0 years was statistically highly significant (p < 0.001). In addition, the correlation coefficient was very high (R = 0.97). In conclusion, the chronological age of pediatric patients can be assessed with high accuracy from CT scout views using a deep neural network.
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Aprendizado Profundo , Adolescente , Humanos , Criança , Pré-Escolar , Estudos Retrospectivos , Tomografia Computadorizada por Raios X , Redes Neurais de ComputaçãoRESUMO
Limited treatment options in patients with intrahepatic cholangiocarcinoma (iCCA) demand the introduction of new, catheter-based treatment options. Especially, 90Y radioembolization may expand therapeutic abilities beyond surgery or chemotherapy. Therefore, the purpose of this study was to identify factors associated with an improved median overall survival (mOS) in iCCA patients receiving radioembolization in a retrospective study at 5 major tertiary-care centers. Methods: In total, 138 radioembolizations in 128 patients with iCCA (female, 47.7%; male, 52.3%; mean age ± SD, 61.1 ± 13.4 y) were analyzed. Clinical data, imaging characteristics, and radioembolization reports, as well as data from RECIST, version 1.1, analysis performed 3, 6, and 12 mo after radioembolization, were collected. mOS was compared among different subgroups using Kaplan-Meier curves and the log-rank test. Results: Radioembolization was performed as first-line treatment in 25.4%, as second-line treatment in 38.4%, and as salvage treatment in 36.2%. In patients receiving first-line, second-line, and salvage radioembolization, the disease control rate was 68.6%, 52.8%, and 54.0% after 3 mo; 31.4%, 15.1%, and 12.0% after 6 mo; and 17.1%, 5.7%, and 6.0% after 1 y, respectively. In patients receiving radioembolization as first-line, second-line, and salvage treatment, mOS was 12.0 mo (95% CI, 7.6-23.4 mo), 11.8 mo (95% CI, 9.1-16.6 mo), and 8.4 mo (95% CI, 6.3-12.7 mo), respectively. No significant differences among the 3 groups were observed (P = 0.15). Hepatic tumor burden did not significantly influence mOS (P = 0.12). Conclusion: Especially in advanced iCCA, second-line and salvage radioembolization may be important treatment options. In addition to ongoing studies investigating the role of radioembolization as first-line treatment, the role of radioembolization in the later treatment stages of the disease demands further attention.
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Neoplasias dos Ductos Biliares , Colangiocarcinoma , Embolização Terapêutica , Neoplasias Hepáticas , Humanos , Masculino , Feminino , Estudos Retrospectivos , Embolização Terapêutica/efeitos adversos , Resultado do Tratamento , Colangiocarcinoma/diagnóstico por imagem , Colangiocarcinoma/radioterapia , Neoplasias Hepáticas/radioterapia , Neoplasias Hepáticas/tratamento farmacológico , Radioisótopos de Ítrio , Ductos Biliares Intra-Hepáticos/patologia , Neoplasias dos Ductos Biliares/radioterapia , Neoplasias dos Ductos Biliares/tratamento farmacológicoRESUMO
In cirrhotic patients with hepatocellular carcinoma (HCC), right-sided radioembolization (RE) with Yttrium-90-loaded microspheres is an established palliative therapy and can be considered a "curative intention" treatment when aiming for sequential tumor resection. To become surgical candidate, hypertrophy of the left liver lobe to > 40% (future liver remnant, FLR) is mandatory, which can develop after RE. The amount of radiation-induced shrinkage of the right lobe and compensatory hypertrophy of the left lobe is difficult for clinicians to predict. This study aimed to utilize machine learning to predict left lobe liver hypertrophy in patients with HCC and cirrhosis scheduled for right lobe RE, with external validation. The results revealed that machine learning can accurately predict relative and absolute volume changes of the left liver lobe after right lobe RE. This prediction algorithm could help to estimate the chances of conversion from palliative RE to curative major hepatectomy following significant FLR hypertrophy.
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Braquiterapia , Carcinoma Hepatocelular , Neoplasias Hepáticas , Humanos , Carcinoma Hepatocelular/radioterapia , Neoplasias Hepáticas/radioterapia , HipertrofiaRESUMO
OBJECTIVES: In radiomics, generic texture and morphological features are often used for modeling. Recently, features extracted from pretrained deep networks have been used as an alternative. However, extracting deep features involves several decisions, and it is unclear how these affect the resulting models. Therefore, in this study, we considered the influence of such choices on the predictive performance. METHODS: On ten publicly available radiomic datasets, models were trained using feature sets that differed in terms of the utilized network architecture, the layer of feature extraction, the used set of slices, the use of segmentation, and the aggregation method. The influence of these choices on the predictive performance was measured using a linear mixed model. In addition, models with generic features were trained and compared in terms of predictive performance and correlation. RESULTS: No single choice consistently led to the best-performing models. In the mixed model, the choice of architecture (AUC + 0.016; p < 0.001), the level of feature extraction (AUC + 0.016; p < 0.001), and using all slices (AUC + 0.023; p < 0.001) were highly significant; using the segmentation had a lower influence (AUC + 0.011; p = 0.023), while the aggregation method was insignificant (p = 0.774). Models based on deep features were not significantly better than those based on generic features (p > 0.05 on all datasets). Deep feature sets correlated moderately with each other (r = 0.4), in contrast to generic feature sets (r = 0.89). CONCLUSIONS: Different choices have a significant effect on the predictive performance of the resulting models; however, for the highest performance, these choices should be optimized during cross-validation.
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PURPOSE: We aimed to evaluate predictors of symptomatic intracranial hemorrhage (sICH) in acute ischemic stroke (AIS) patients following thrombectomy due to anterior large vessel occlusion (LVO). METHODS: Data on stroke patients from January 2018 to December 2020 in a tertiary care centre were retrospectively analysed. sICH was defined as intracranial hemorrhage associated with a deterioration of at least four points in the National Institutes of Health Stroke Scale (NIHSS) score or hemorrhage leading to death. A smoothed ridge regression model was run to analyse the impact of 15 variables on their association with sICH. RESULTS: Of the 174 patients (median age 77, 41.4% male), sICH was present in 18 patients. Short procedure time from groin puncture to reperfusion (per 10 min OR 1.24; 95% CI 1.071-1.435; p = 0.004) and complete reperfusion (TICI 3) (OR 0.035; 95% CI 0.003-0.378; p = 0.005) were significantly associated with a lower risk of sICH. On the contrary, successful reperfusion (TICI 3 and TICI 2b) was not associated with a lower risk of sICH (OR 0.508; 95% CI 0.131-1.975, p = 0.325). Neither the total time from symptom onset to reperfusion nor the intravenous thrombolysis was a predictor of sICH (per 10 min OR 1.0; 95% CI 0.998-1.001, p = 0.745) (OR 1.305; 95% CI 0.338-5.041, p = 0.697). CONCLUSION: Our findings addressed the paramount importance of short procedure time and complete reperfusion to minimize sICH risk. The total ischemic time from onset to reperfusion was not a predictor of sICH.
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BACKGROUND: Radiomics is a noninvasive method using machine learning to support personalised medicine. Preprocessing filters such as wavelet and Laplacian-of-Gaussian filters are commonly used being thought to increase predictive performance. However, the use of preprocessing filters increases the number of features by up to an order of magnitude and can produce many correlated features. Both substantially increase the dataset complexity, which in turn makes modeling with machine learning techniques more challenging, possibly leading to poorer performance. We investigated the impact of these filters on predictive performance. METHODS: Using seven publicly available radiomic datasets, we measured the impact of adding features preprocessed with eight different preprocessing filters to the unprocessed features on the predictive performance of radiomic models. Modeling was performed using five feature selection methods and five classifiers, while predictive performance was measured using area-under-the-curve at receiver operating characteristics analysis (AUC-ROC) with nested, stratified 10-fold cross-validation. RESULTS: Significant improvements of up to 0.08 in AUC-ROC were observed when all image preprocessing filters were applied compared to using only the original features (up to p = 0.024). Decreases of -0.04 and -0.10 were observed on some data sets, but these were not statistically significant (p > 0.179). Tuning of the image preprocessing filters did not result in decreases in AUC-ROC but further improved results by up to 0.1; however, these improvements were not statistically significant (p > 0.086) except for one data set (p = 0.023). CONCLUSIONS: Preprocessing filters can have a significant impact on the predictive performance and should be used in radiomic studies.