Imaging of Post Transthoracic Needle Biopsy Complications

Dubé, Jean-Philippe; Azzi, Ziad; Semionov, Alexandre; Sayegh, Karl; Kosiuk, John; Pressacco, Joséphine

doi:10.1016/j.carj.2018.08.006

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…These methods do have certain drawbacks, though. First of all, these are intrusive tests that have a risk of several complications, including bleeding, air embolism, and pneumothorax ( 7 ). In a population-level retrospective study, the percentage of patients who developed comorbidities within 3 days after transthoracic needle biopsy (TTNB) reached 25.8%, with the top three being pneumothorax at 23.3%, hemorrhage, and air embolism ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach

Kuang,

Zhang,

Zhang

et al. 2024

Front. Med.

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ObjectiveThis research aims to develop and assess the performance of interpretable machine learning models for diagnosing three histological subtypes of non-small cell lung cancer (NSCLC) utilizing CT imaging data.MethodsA retrospective cohort of 317 patients diagnosed with NSCLC was included in the study. These individuals were randomly segregated into two groups: a training set comprising 222 patients and a validation set with 95 patients, adhering to a 7:3 ratio. A comprehensive extraction yielded 1,834 radiomic features. For feature selection, statistical methodologies such as the Mann–Whitney U test, Spearman’s rank correlation, and one-way logistic regression were employed. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized. The study designed three distinct models to predict adenocarcinoma (ADC), squamous cell carcinoma (SCC), and large cell carcinoma (LCC). Six different classifiers, namely Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, eXtreme Gradient Boosting (XGB), and LightGBM, were deployed for model training. Model performance was gauged through accuracy metrics and the area under the receiver operating characteristic (ROC) curves (AUC). To interpret the diagnostic process, the Shapley Additive Explanations (SHAP) approach was applied.ResultsFor the ADC, SCC, and LCC groups, 9, 12, and 8 key radiomic features were selected, respectively. In terms of model performance, the XGB model demonstrated superior performance in predicting SCC and LCC, with AUC values of 0.789 and 0.848, respectively. For ADC prediction, the Random Forest model excelled, showcasing an AUC of 0.748.ConclusionThe constructed machine learning models, leveraging CT imaging, exhibited robust predictive capabilities for SCC, LCC, and ADC subtypes of NSCLC. These interpretable models serve as substantial support for clinical decision-making processes.

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Section: Introductionmentioning

confidence: 99%

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach

Kuang,

Zhang,

Zhang

et al. 2024

Front. Med.

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“…Ki-67 detection relies on pathological examination. Needle biopsy is a common method to obtain pathological tissue, but it is an invasive examination and has certain disadvantages, such as poor patient compliance, possible surgical risks, and needle tract transfer (9,10). With the formation of a multidisciplinary and multimethod comprehensive treatment model for liver cancer, a noninvasive preoperative method to predict Ki-67 status is significant in the treatment and prognostic management of patients.…”

Section: Introductionmentioning

confidence: 99%

Nomogram development and validation to predict Ki-67 expression of hepatocellular carcinoma derived from Gd-EOB-DTPA-enhanced MRI combined with T1 mapping

et al. 2022

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ObjectiveAs an important biomarker to reflect tumor cell proliferation and tumor aggressiveness, Ki-67 is closely related to the high early recurrence rate and poor prognosis, and pretreatment evaluation of Ki-67 expression possibly provides a more accurate prognosis assessment and more better treatment plan. We aimed to develop a nomogram based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) combined with T1 mapping to predict Ki-67 expression in hepatocellular carcinoma (HCC).MethodsThis two-center study retrospectively enrolled 148 consecutive patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI T1 mapping and surgically confirmed HCC from July 2019 to December 2020. The correlation between quantitative parameters from T1 mapping, ADC, and Ki-67 was explored. Three cohorts were constructed: a training cohort (n = 73) and an internal validation cohort (n = 31) from Shunde Hospital of Southern Medical University, and an external validation cohort (n = 44) from the Sixth Affiliated Hospital, South China University of Technology. The clinical variables and MRI qualitative and quantitative parameters associational with Ki-67 expression were analyzed by univariate and multivariate logistic regression analyses. A nomogram was developed based on these associated with Ki-67 expression in the training cohort and validated in the internal and external validation cohorts.ResultsT1rt-Pre and T1rt-20min were strongly positively correlated with Ki-67 (r = 0.627, r = 0.607, P < 0.001); the apparent diffusion coefficient value was moderately negatively correlated with Ki-67 (r = -0.401, P < 0.001). Predictors of Ki-67 expression included in the nomogram were peritumoral enhancement, peritumoral hypointensity, T1rt-20min, and tumor margin, while arterial phase hyperenhancement (APHE) was not a significant predictor even included in the regression model. The nomograms achieved good concordance indices in predicting Ki-67 expression in the training and two validation cohorts (0.919, 0.925, 0.850), respectively.ConclusionsT1rt-Pre and T1rt-20min had a strong positive correlation with the Ki-67 expression in HCC, and Gd-EOB-DTPA enhanced MRI combined with T1 mapping-based nomogram effectively predicts high Ki-67 expression in HCC.

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“…However, this method has some limitations. First, histological examination is an invasive procedure that may cause serious complications such as pneumothorax ( 8 ), hemorrhage ( 9 ), and air embolism ( 10 ). Second, needle biopsy increases the risk of TB dissemination ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

The value of dual-energy spectral CT in differentiating solitary pulmonary tuberculosis and solitary lung adenocarcinoma

Zhang

et al. 2022

Front. Oncol.

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BackgroundTo explore the value of dual-energy spectral CT in distinguishing solitary pulmonary tuberculosis (SP-TB) from solitary lung adenocarcinoma (S-LUAD).MethodsA total of 246 patients confirmed SP-TB (n = 86) or S-LUAD (n = 160) were retrospectively included. Spectral CT parameters include CT40keV value, CT70keV value, iodine concentration (IC), water concentration (WC), effective atomic number (Zeff), and spectral curve slope (λ70keV). Data were measured during the arterial phase (AP) and venous phase (VP). Chi-square test was used to compare categorical variables, Wilcoxon rank-sum test was used to compare continuous variables, and a two-sample t-test was used to compare spectral CT parameters. ROC curves were used to calculate diagnostic efficiency.ResultsThere were significant differences in spectral CT quantitative parameters (including CT40keV value [all P< 0.001] , CT70keV value [all P< 0.001], λ70keV [P< 0.001, and P = 0.027], Zeff [P =0.015, and P = 0.001], and IC [P =0.002, and P = 0.028]) between the two groups during the AP and VP. However, WC (P = 0.930, and P = 0.823) was not statistically different between the two groups. The ROC curve analysis showed that the AUC in the AP and VP was 90.9% (95% CI, 0.873-0.945) and 83.4% (95% CI, 0.780-0.887), respectively. The highest diagnostic performance (AUC, 97.6%; 95% CI, 0.961-0.991) was achieved when all spectral CT parameters were combined with clinical variables.ConclusionDual-energy spectral CT has a significant value in distinguishing SP-TB from S-LUAD.

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Imaging of Post Transthoracic Needle Biopsy Complications

Cited by 8 publications

References 21 publications

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach

Nomogram development and validation to predict Ki-67 expression of hepatocellular carcinoma derived from Gd-EOB-DTPA-enhanced MRI combined with T1 mapping

The value of dual-energy spectral CT in differentiating solitary pulmonary tuberculosis and solitary lung adenocarcinoma

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