The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

Gao, Jianxiong; Niu, Rong; Shi, Yunmei; Shao, Xiaoliang; Jiang, Zhenxing; Ge, Xinyu; Wang, Yuetao; Shao, Xiaonan

doi:10.1186/s13550-023-00977-4

Cited by 11 publications

(5 citation statements)

References 51 publications

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“…The expression of BATF could be a potential selective criterion of anti-cancer therapeutics, which could only be detected after surgery and could not be monitored repeatedly and dynamically. As a whole-body molecular imaging modality, 18 F-FDG PET/CT plays an important role in tumor diagnosis, staging and treatment outcome in CRC [ 35 ]. The PET-related parameters include the SUV max , MTV, and TLG, which were reported to be positively correlated with the malignancy of multiple tumors, including CRC [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

BATF promotes tumor progression and association with FDG PET-derived parameters in colorectal cancer

Lu,

Liu,

Feng

et al. 2024

J Transl Med

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Purpose The purpose of the study was to evaluate the expression and function of basic leucine zipper ATF-like transcription factor (BATF) in colorectal cancer (CRC), and its correlation with 2-deoxy-2[18F]fluoro-d-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) parameters. Methods The TIMER database, GEPIA database, TCGA, and GEO database were used to analyze the expression profile of BATF in human cancers. The reverse transcription‑quantitative PCR and western blot analyses were used to evaluate the mRNA level and protein expression in different CRC cell lines. The expression of BATF in SW620 and HCT116 cells was silenced and cell counting kit-8 assays and clonogenic assay were utilized to evaluate the role of BATF in CRC proliferation. The expression of tumor BATF and glucose transporter 1 (GLUT-1) were examined using immunohistochemical tools in 37 CRC patients undergoing preoperative 18F-FDG PET/CT imaging. The correlation between the PET/CT parameters and immunohistochemical result was evaluated. Results In database, BATF was highly expressed in pan-cancer analyses, including CRC, and was associated with poor prognosis in CRC. In vitro, the results showed that knocking down of BATF expression could inhibit the proliferation of SW620 and HCT116 cells. In CRC patients, BATF expression was upregulated in tumor tissues compared with matched para-tumoral tissues, and was related with gender and Ki-67 levels. BATF expression was positively related to GLUT-1 expression and PET/CT parameters, including tumor size, maximum standard uptake value, metabolic tumor volume, and total lesion glycolysis. The multiple logistic analyses showed that SUVmax was an independent predictor of BATF expression. With 15.96 g/cm3 as the cutoff, sensitivity was 85.71%, specificity 82.61%, and area-under-the-curve 0.854. Conclusion BATF may be an oncogene associated with 18F-FDG PET/CT parameters in CRC. SUVmax may be an independent predictor of BATF expression.

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Section: Discussionmentioning

confidence: 99%

BATF promotes tumor progression and association with FDG PET-derived parameters in colorectal cancer

Lu,

Liu,

Feng

et al. 2024

J Transl Med

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“…Several reports have examined the usefulness of 18 F-FDG PET/CT radiomics-based ML analysis for predicting gene mutation. Previous studies commonly showed that 18 F-FDG PET/CT radiomics-based ML analysis had a promising performance for predicting gene mutation [ 33 – 39 ] (Table 1 ). Gao et al [ 33 ] constructed radiomics-based models based on 18 F-FDG PET/CT features using ML to predict EGFR mutation status in patients with lung ADC.…”

Section: Clinical Application Of 18 F-fdg Pet/ct R...mentioning

confidence: 99%

“…Previous studies commonly showed that 18 F-FDG PET/CT radiomics-based ML analysis had a promising performance for predicting gene mutation [ 33 – 39 ] (Table 1 ). Gao et al [ 33 ] constructed radiomics-based models based on 18 F-FDG PET/CT features using ML to predict EGFR mutation status in patients with lung ADC. Results showed that the ML model with the random forest (RF) algorithm using combined clinical data, CT-radiomics and PET-radiomics had the highest performance, with an AUC of 0.730.…”

Section: Clinical Application Of 18 F-fdg Pet/ct R...mentioning

confidence: 99%

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

et al. 2023

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Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.

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“…Also, our previous study ( 12 ) has shown that machine learning model based on PET/CT handcrafted radiomics features (HRFs) achieved a good prediction performance in the identification of EGFR mutation status and subtypes in lung adenocarcinoma. Recently, the value of PET/CT-based HRFs in predicting EGFR mutation status, EGFR subtypes and prognosis had been well demonstrated ( 12 – 18 ), which involved different feature selection methods and machine learning algorithms. These different radiomics pipelines may lead to various predictive performance ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study

Zuo

Liu

et al. 2023

Front. Oncol.

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PurposeTo develop and interpret optimal predictive models to identify epidermal growth factor receptor (EGFR) mutation status and subtypes in patients with lung adenocarcinoma based on multicentric 18F-FDG PET/CT data, and further construct a prognostic model to predict their clinical outcome.MethodsThe 18F-FDG PET/CT imaging and clinical characters of 767 patients with lung adenocarcinoma from 4 cohorts were collected. Seventy-six radiomics candidates using cross-combination method to identity EGFR mutation status and subtypes were built. Further, Shapley additive explanations and local interpretable model-agnostic explanations were used for optimal models’ interpretation. Moreover, in order to predict the overall survival, a multivariate Cox proportional hazard model based on handcrafted radiomics features and clinical characteristics was constructed. The predictive performance and clinical net benefit of the models were evaluated via area under receiver operating characteristic (AUC), C-index and decision curve analysis. ResultsAmong the 76 radiomics candidates, light gradient boosting machine classifier (LGBM) combined with recursive feature elimination wrapped LGBM feature selection method achieved best performance in predicting EGFR mutation status (AUC reached 0.80, 0.61, 0.71 in the internal test cohort and two external test cohorts, respectively). And extreme gradient boosting classifier combined with support vector machine feature selection method achieved best performance in predicting EGFR subtypes (AUC reached 0.76, 0.63, 0.61 in the internal test cohort and two external test cohorts, respectively). The C-index of the Cox proportional hazard model achieved 0.863.ConclusionsThe integration of cross-combination method and the external validation from multi-center data achieved a good prediction and generalization performance in predicting EGFR mutation status and its subtypes. The combination of handcrafted radiomics features and clinical factors achieved good performance in predicting prognosis. With the urgent needs of multicentric 18F-FDG PET/CT trails, robust and explainable radiomics models have great potential in decision making and prognosis prediction of lung adenocarcinoma.

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The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

Cited by 11 publications

References 51 publications

BATF promotes tumor progression and association with FDG PET-derived parameters in colorectal cancer

BATF promotes tumor progression and association with FDG PET-derived parameters in colorectal cancer

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study

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