Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach

Zhou, Yi; Ma, Xuelei; Zhang, Ting; Wang, Jian; Zhang, Tao; Tian, Rong

doi:10.1007/s00259-021-05220-7

Cited by 55 publications

(35 citation statements)

References 50 publications

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“…6 To date, radiomics has been applied in various oncology researches for precise clinical decision, treatment response, and prognosis predication. [7][8][9] Moreover, radiogenomics emerges to explore the relationships between radiomics features and genomic characteristics, with the goal of noninvasively uncovering the underlying biological heterogeneity most strongly associated with clinical outcomes. 10 However, radiogenomics research regarding NPC is not available.…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance imaging‐based radiogenomics analysis for predicting prognosis and gene expression profile in advanced nasopharyngeal carcinoma

Gao

Mao

et al. 2021

Head & Neck

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Background: To establish a radiomics nomogram for survival prediction and determine if genomic data were related to radiomics signature in advanced nasopharyngeal carcinoma (NPC).Methods: Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1WI) in 316 patients. A progression-free survival (PFS) nomogram was developed and validated by the combination of the radiomics signature and clinicopathologic factors. Whole transcriptomics sequencing was performed in pretreatment tumor samples; correlation of gene expression and radiomics signature was further investigated.Results: A 24-feature-combined radiomics signature was highly correlated with PFS; its integration with clinical predictors showed good prediction performance in the training and the validation cohort (C-index: 0.80 and 0.73). A significant correlation was observed between certain gene expression and Radscore, especially the mRNA expression of CDKL2, PLIN5, and SPAG1. Conclusion:As a noninvasive method, the MRI-based radiomics signature might enable the pretreatment prediction of prognosis and gene expressions profile in advanced NPC.

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Section: Introductionmentioning

confidence: 99%

Magnetic resonance imaging‐based radiogenomics analysis for predicting prognosis and gene expression profile in advanced nasopharyngeal carcinoma

Gao

Mao

et al. 2021

Head & Neck

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“…Radiomics features represented tumor heterogeneity and were extracted from the entire ROI; they were not just limited to the biopsy site (17). Previous studies demonstrated that radiomics plays a role in differentiating between primary and metastatic tumors (25)(26)(27)(28)(29). In particular, CT radiomics features combined with positron emission tomography (PET) features can accurately distinguish between primary and metastatic lung cancers (26,27).…”

Section: Discussionmentioning

confidence: 99%

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions

Zhong

Liu

et al. 2022

Front. Oncol.

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BackgroundThe objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs).MethodsThis retrospective study enrolled 252 malignant pulmonary nodules with histopathologically confirmed SPLCs or PMs and randomly assigned them to a training or validation cohort. Clinical data were collected from the electronic medical records system. The imaging and radiomics features of each nodule were extracted from CT images.ResultsA rad-score was generated from the training cohort using the least absolute shrinkage and selection operator regression. A clinical and radiographic model was constructed using the clinical and imaging features selected by univariate and multivariate regression. A nomogram composed of clinical-radiographic factors and a rad-score were developed to validate the discriminative ability. The rad-scores differed significantly between the SPLC and PM groups. Sixteen radiomics features and four clinical-radiographic features were selected to build the final model to differentiate between SPLCs and PMs. The comprehensive clinical radiographic–radiomics model demonstrated good discriminative capacity with an area under the curve of the receiver operating characteristic curve of 0.9421 and 0.9041 in the respective training and validation cohorts. The decision curve analysis demonstrated that the comprehensive model showed a higher clinical value than the model without the rad-score.ConclusionThe proposed model based on clinical data, imaging features, and radiomics features could accurately discriminate SPLCs from PMs. The model thus has the potential to support clinicians in improving decision-making in a noninvasive manner.

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“…In this study, although we also focused on the mutation prediction, we conducted multiple combinations of machine learning methods, and the random forest models had high prediction accuracy for ALK (AUC = 0.879), BRAF (AUC = 0.847), EGFR (AUC = 0.855), and ROS1 (AUC = 0.848) aberrations. In our previous radiomics study of lung cancer, random forest also had good classification accuracy for primary and metastatic lung lesions (Zhou et al, 2021). Meanwhile, the models were trained to predict the transcriptional subtypes of LUAD for the first time, with AUCs from 0.861 to 0.897.…”

Section: Discussionmentioning

confidence: 99%

Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma

Chen

Zeng

et al. 2021

Front. Cell Dev. Biol.

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Histopathological images and omics profiles play important roles in prognosis of cancer patients. Here, we extracted quantitative features from histopathological images to predict molecular characteristics and prognosis, and integrated image features with mutations, transcriptomics, and proteomics data for prognosis prediction in lung adenocarcinoma (LUAD). Patients obtained from The Cancer Genome Atlas (TCGA) were divided into training set (n = 235) and test set (n = 235). We developed machine learning models in training set and estimated their predictive performance in test set. In test set, the machine learning models could predict genetic aberrations: ALK (AUC = 0.879), BRAF (AUC = 0.847), EGFR (AUC = 0.855), ROS1 (AUC = 0.848), and transcriptional subtypes: proximal-inflammatory (AUC = 0.897), proximal-proliferative (AUC = 0.861), and terminal respiratory unit (AUC = 0.894) from histopathological images. Moreover, we obtained tissue microarrays from 316 LUAD patients, including four external validation sets. The prognostic model using image features was predictive of overall survival in test and four validation sets, with 5-year AUCs from 0.717 to 0.825. High-risk and low-risk groups stratified by the model showed different survival in test set (HR = 4.94, p < 0.0001) and three validation sets (HR = 1.64–2.20, p < 0.05). The combination of image features and single omics had greater prognostic power in test set, such as histopathology + transcriptomics model (5-year AUC = 0.840; HR = 7.34, p < 0.0001). Finally, the model integrating image features with multi-omics achieved the best performance (5-year AUC = 0.908; HR = 19.98, p < 0.0001). Our results indicated that the machine learning models based on histopathological image features could predict genetic aberrations, transcriptional subtypes, and survival outcomes of LUAD patients. The integration of histopathological images and multi-omics may provide better survival prediction for LUAD.

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Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach

Cited by 55 publications

References 50 publications

Magnetic resonance imaging‐based radiogenomics analysis for predicting prognosis and gene expression profile in advanced nasopharyngeal carcinoma

Magnetic resonance imaging‐based radiogenomics analysis for predicting prognosis and gene expression profile in advanced nasopharyngeal carcinoma

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions

Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma

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