&lt;p&gt;Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC&lt;/p&gt;

Wen, Qiang; Yang, Zhe; Zhu, Jian; Qiu, Qingtao; Dai, Hui; Feng, Alei; Xing, Ligang

doi:10.2147/ott.s261068

Cited by 39 publications

(31 citation statements)

References 56 publications

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“…Preoperatively assessing the immune status of patients ca cians to identify which patients are suitable for immunotherapy, thereb ment efficiency and overall survival rate. The expression level of PD portant role in guiding the selection of ICI therapy for liver cancer patie of radiomics features for preoperatively assessing PD-L1 expression has attracted significant attention in the literature [46][47][48][49][50][51]. For example combined quantitative and qualitative radiomics features to preoperati pression of PD-L1 biomarker in resected HCC.…”

Section: Discussionmentioning

confidence: 99%

“…The expression level of PD-L1 plays an important role in guiding the selection of ICI therapy for liver cancer patients. The utilization of radiomics features for preoperatively assessing PD-L1 expression before ICI therapy has attracted significant attention in the literature [46][47][48][49][50][51]. For example, Hectors et al [46] combined quantitative and qualitative radiomics features to preoperatively assess the expression of PD-L1 biomarker in resected HCC.…”

Section: Discussionmentioning

confidence: 99%

“…However, the main drawbacks of deep learning are the requirement of massive data for training classifiers and the poor interpretability of features, which limits its application. Recently, there have been many studies using radiomics or deep learning to predict the PD-L1 expression level in HCC patients [45][46][47][48][49][50][51]. All these studies gave good exploration for predicting the expression level of PD-L1, but there are still relatively few studies exploiting deep learning and radiomics features for operatively assessing the PD-L1 expression status.…”

Section: Introductionmentioning

confidence: 99%

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Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features

et al. 2021

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To assess if quantitative integrated deep learning and radiomics features can predict the PD-L1 expression level in preoperative MRI of hepatocellular carcinoma (HCC) patients. The data in this study consist of 103 hepatocellular carcinoma patients who received immunotherapy in a single center. These patients were divided into a high PD-L1 expression group (30 patients) and a low PD-L1 expression group (73 patients). Both radiomics and deep learning features were extracted from their MRI sequence of T2-WI, which were merged into an integrative feature space for machine learning for the prediction of PD-L1 expression. The five-fold cross-validation was adopted to validate the performance of the model, while the AUC was used to assess the predictive ability of the model. Based on the five-fold cross-validation, the integrated model achieved the best prediction performance, with an AUC score of 0.897 ± 0.084, followed by the deep learning-based model with an AUC of 0.852 ± 0.043 then the radiomics-based model with AUC of 0.794 ± 0.035. The feature set integrating radiomics and deep learning features is more effective in predicting PD-L1 expression level than only one feature type. The integrated model can achieve fast and accurate prediction of PD-L1 expression status in preoperative MRI of HCC patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features

et al. 2021

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“…CT radiomics features have also been successfully used to identify programmed death-ligand 1 and CD8+ tumor infiltrating lymphocyte expression levels in patients with ESCC. Such analyses, performed on pretreatment CT images, allowed for better patient stratification and selection of candidates for immune checkpoint inhibitor therapy[ 21 ]. Finally, other encouraging applications of CT radiomics analysis in ESCC are pretreatment local staging[ 22 ] and assessment of lymph node status[ 23 , 24 ] with advantage over morphological features and size criteria.…”

Section: Applications Of Ai In Gi Radiologymentioning

confidence: 99%

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

Berbís¹,

Aneiros‐Fernández²,

Olivares³

et al. 2021

WJG

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The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization ( i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

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“…They may extract spatial and temporal features from images that are useful in predicting the underlying molecular mechanisms, the tumor-immune microenvironment, and clinical outcome. Studies dealing with glioma, esophagus, lung, and liver cancers have shown that several imaging features extracted by radiomics were closely related to CD8 + TIL density (15)(16)(17)(18)(19). While reports are predicting clinicopathological results from tissue sections in PDAC (20)(21)(22), so far, there are no radiomic studies revealing the immune environment in PDAC.…”

Section: Introductionmentioning

confidence: 99%

XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma

Shi

Liu

et al. 2021

Front. Oncol.

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ObjectivesThis study constructed and validated a machine learning model to predict CD8+ tumor-infiltrating lymphocyte expression levels in patients with pancreatic ductal adenocarcinoma (PDAC) using computed tomography (CT) radiomic features.Materials and MethodsIn this retrospective study, 184 PDAC patients were randomly assigned to a training dataset (n =137) and validation dataset (n =47). All patients were divided into CD8+ T-high and -low groups using X-tile plots. A total of 1409 radiomics features were extracted from the segmentation of regions of interest, based on preoperative CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. The extreme gradient boosting classifier (XGBoost) was developed using a training set consisting of 137 consecutive patients admitted between January 2017 and December 2017. The model was validated in 47 consecutive patients admitted between January 2018 and April 2018. The performance of the XGBoost classifier was determined by its discriminative ability, calibration, and clinical usefulness.ResultsThe cut-off value of the CD8+ T-cell level was 18.69%, as determined by the X-tile program. A Kaplan−Meier analysis indicated a correlation between higher CD8+ T-cell levels and better overall survival (p = 0.001). The XGBoost classifier showed good discrimination in the training set (area under curve [AUC], 0.75; 95% confidence interval [CI]: 0.67–0.83) and validation set (AUC, 0.67; 95% CI: 0.51–0.83). Moreover, it showed a good calibration. The sensitivity, specificity, accuracy, positive and negative predictive values were 80.65%, 60.00%, 0.69, 0.63, and 0.79, respectively, for the training set, and 80.95%, 57.69%, 0.68, 0.61, and 0.79, respectively, for the validation set.ConclusionsWe developed a CT-based XGBoost classifier to extrapolate the infiltration levels of CD8+ T-cells in patients with PDAC. This method could be useful in identifying potential patients who can benefit from immunotherapies.

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<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

Cited by 39 publications

References 56 publications

Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features

Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma

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