Development of unenhanced CT-based imaging signature for BAP1 mutation status prediction in malignant pleural mesothelioma: Consideration of 2D and 3D segmentation

Xie, Xing; Liu, Si-Yun; Chen, Jianyou; Zhao, Yi; Jiang, Jie; Wu, Li; Zhang, Xingwen; Wu, Yi; Duan, Hui; He, Bing; Luo, Heng; Han, Dan

doi:10.1016/j.lungcan.2021.04.023

Cited by 14 publications

(8 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For NSCLC, signatures of CT, fusion characteristics of ceCT, and PET forecasted EGFR mutations and anaplastic lymphoma kinase rearrangements, EGFR and Kirsten rat sarcoma virus positivity ( 48 , 49 , 66 ), respectively. Preoperative radiomic analysis of MR and CT images was successfully applied to differentiate isocitrate dehydrogenase and 1p19q mutations in glioma and BAP1 mutation in malignant pleural mesothelioma ( 114 , 118 , 119 ). In breast cancer, deep learning-based radiomics was successfully implemented to identify BRCA, and six different types of positive statuses from WSIs correlated with targeted therapy ( 150 , 151 ).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

show abstract

Section: Discussionmentioning

confidence: 99%

“…Xie et al. ( 114 ) claimed that 3D non-ceCT attributes outperformed 2D in predicting BRCA1-associated protein 1 (BAP1) status in patients with malignant pleural mesothelioma. Fewer studies reported on 2D and 3D radiomic analysis, and their findings are conflicting and insufficient to generalize to all studies.…”

Section: Some Case Studies and Applicationsmentioning

confidence: 99%

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…Recently, new diagnostic tools, such as radiogenomics, have helped identify the potential correlation between imaging findings and tumour genotype. CT-based 3D radiomics signatures have potential as noninvasive markers for the prediction of BAP1 mutation status in patients with mesothelioma MPM as well as for differential diagnosis and prediction of response to therapy and prognosis ( 24 ).…”

Section: Discussionmentioning

confidence: 99%

Case report: Mesothelioma and BAP1 tumor predisposition syndrome: Implications for public health

et al. 2022

View full text Add to dashboard Cite

BRCA-1 associated protein 1 (BAP1) tumour predisposition syndrome (TPDS) is a hereditary condition characterised by germline mutation of the tumour suppressor BAP1. This disorder is associated with the development of various benign and malignant tumours, mainly involving the skin, eyes, kidneys, and mesothelium. In this article, we report the case of a man recruited through the Apulia (Southern Italy) Mesothelioma Regional Operational Centre of the National Register of Mesotheliomas, who suffered from uveal melanoma, renal cancer, and mesothelioma, and a familial cluster of BAP1 germline mutations demonstrated by molecular analyses. The family members of the proband developed multiple malignancies. As tumours arising in this context have specific peculiarities in terms of clinical behaviour, identification of this condition through appropriate genetic counselling should be considered for adequate primary, secondary, and tertiary prevention measures for offspring.

show abstract

“…have shown that 3D segmentation has better performance than 2D segmentation in distinguishing ovarian borderline tumors and epithelial cancers ( 39 ). Several previous studies have also demonstrated that radiomic features based on 3D segmentation are preferably repeatable ( 40 ) and more insensitive to manual segmentation variability ( 41 ). Furthermore, our study chose the portal venous phase CT images to extract radiomic features and demonstrated that they had favorable predictive performance.…”

Section: Discussionmentioning

confidence: 99%

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors

Zhang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

ObjectivesThis study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.MethodsA total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.ResultsThe MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p<0.001).ConclusionsMachine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.

show abstract

Development of unenhanced CT-based imaging signature for BAP1 mutation status prediction in malignant pleural mesothelioma: Consideration of 2D and 3D segmentation

Cited by 14 publications

References 47 publications

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Case report: Mesothelioma and BAP1 tumor predisposition syndrome: Implications for public health

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors

Contact Info

Product

Resources

About