Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma

Nie, Pei; Yang, Guangli; Wang, Ning; Yan, Lei; Miao, Wenjie; Duan, Yanli; Wang, Yanli; Gong, Aidi; Zhao, Yongliang; Wu, Jie; Zhang, Chuantao; Wang, Maolong; Cui, Jingjing; Yu, Mingming; Li, Dacheng; Sun, Yanqin; Wang, Yangyang; Wang, Zhenguang

doi:10.1007/s00259-020-04747-5

Cited by 45 publications

(26 citation statements)

References 38 publications

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“…This implies that incorporating MRI or PET/CT images into our model to develop a multimodal radiomics model may improve the predictive performance. Nie et al (28) found that the prediction model developed using CT morphology, 2D-RS and SUV values (AUCs,0.851 and 0.838, in training and testing cohort) performed better than the model without SUV values (0.796,0.822), reflecting the incremental value of metabolic parameters in the prediction of LVI in LAC patients. The difference from our study was that the authors adopted 2D-ROI (CT) for radiomics feature extraction and model building.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, radiomics has been proven to be potential clinical value in predicting intra-tumoral LVI. Nie et al (28) developed a radiomics nomogram incorporating Rad-score, clinical and PET/ CT parameters to predict LVI in lung adenocarcinoma, which showed good predictive performance. Chen et al (15) found that radiomics features based on CECT could serve as potential markers for predicting LVI and PFS in gastric cancer.…”

Section: Introductionmentioning

confidence: 99%

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Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Wang

Yang

et al. 2021

Front. Oncol.

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ObjectivesTo develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians.Patients and MethodsThis retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC.ResultsIn the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P<0.001). Patients with LVI had higher clinical N stage based on CECT (cN stage) than patients without LVI (P<0.001). The ROC analysis showed that both the radiomics model (AUC values were 0.847 and 0.826 in the training and testing cohort, respectively) and the combined model (0.876 and 0.867, respectively) performed better than the clinical model (0.775 and 0.798, respectively), with the combined model exhibiting the best performance.ConclusionsThe combined model incorporating radiomics features and clinical CT predictors may potentially predict the LVI status in ESCC and provide support for clinical treatment decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Wang

Yang

et al. 2021

Front. Oncol.

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“…While there are currently no studies published on the use of AI on 18F-FDG PET images for BC, promising results of its application using CT and MRI images have been reported in terms of predicting the depth of invasion of the primary tumor (83), grade (84), local and systemic staging (85), and assessment of treatment response (86). Additionally, AI based on PET/CT images has been used in other malignancies to predict nodal disease (87), risk stratification (88), treatment response (89), and patient outcomes (90). The main advantages of using AI is its potential reproducibility, as compared to the inherently subjective interpretation of medical images by physicians as well as the ability to harness large quantities of data that may escape the pattern recognition abilities of humans.…”

Section: Rationale For Artificial Intelligencementioning

confidence: 99%

The Role of 18F-FDG PET/CT in Guiding Precision Medicine for Invasive Bladder Carcinoma

et al. 2020

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Bladder cancer (BC) is the 10th most common cancer worldwide. Approximately one quarter of patients with BC have muscle-invasive disease (MIBC). Muscle-invasive disease carries a poor prognosis and choosing the optimal treatment option is critical to improve patients’ outcomes. Ongoing research supports the role of 2-deoxy-2-(18F)fluoro- D -glucose positron emission tomography (18F-FDG PET) in guiding patient-specific management decisions throughout the course of MIBC. As an imaging modality, 18F-FDG PET is acquired simultaneously with either computed tomography (CT) or MRI to offer a hybrid approach combining anatomical and metabolic information that complement each other. At initial staging, 18F-FDG PET/CT enhances the detection of extravesical disease, particularly in patients classified as oligometastatic by conventional imaging. 18F-FDG PET/CT has value in monitoring response to neoadjuvant and systemic chemotherapy, as well as in localizing relapse after treatment. In the new era of immunotherapy, 18F-FDG PET/CT may also be useful to monitor treatment efficacy as well as to detect immune-related adverse events. With the advent of artificial intelligence techniques such as radiomics and deep learning, these hybrid medical images can be mined for quantitative data, providing incremental value over current standard-of-care clinical and biological data. This approach has the potential to produce a major paradigm shift toward data-driven precision medicine with the ultimate goal of personalized medicine. In this review, we highlight current literature reporting the role of 18F-FDG PET in supporting personalized management decisions for patients with MIBC. Specific topics reviewed include the incremental value of 18F-FDG PET in prognostication, pre-operative planning, response assessment, prediction of recurrence, and diagnosing drug toxicity.

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“…Radiomics is more comprehensive than morphological visual analysis and can quantitatively reveal tumor heterogeneity. Various types of medical images can be analyzed to help with diagnosis and treatment and support traditional diagnostic methods (14,16,31,32). Considering that different mutation accompanied by different enhancement ratio, heterogeneous blood supply (33).…”

Section: Discussionmentioning

confidence: 99%

“…Medical imaging has been playing an increasing key role in personalized precision medicine. Radiomics has been applied to oncology studies (13) and has been widely used in diagnosis (14), prognostic evaluation (15), prediction of biological behaviors (16), and even genetic prediction (11,17).…”

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confidence: 99%

Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

Liu¹,

Wang²,

Liu³

et al. 2020

Preprint

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Background Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors.Methods We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase (A-phase), venous phase (V-phase), delayed phase (D-phase), and combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC).Results The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval (CI), 0.640–0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397–0.790), 0.715 (95% CI, 0.506–0.873), and 0.679 (95% CI, 0.468–0.847), respectively, with the validation set.Conclusion The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

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Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma

Cited by 45 publications

References 38 publications

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

The Role of 18F-FDG PET/CT in Guiding Precision Medicine for Invasive Bladder Carcinoma

Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

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