Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods

Han, Xianbo; Yang, Jing; Luo, Jingwen; Chen, Pengan; Zhang, Zilong; Alu, Aqu; Xiao, Yu; Ma, Xuelei

doi:10.3389/fonc.2021.606677

Cited by 14 publications

(8 citation statements)

References 43 publications

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“…Recent development in texture analysis (TA), also known as radiomics, has led to a renewed solution to this demanding problem. TA, a mathematical method to quantify the heterogeneity in images by calculating the voxel intensity, has been applied to medical imaging (including computed tomography and MRI) and received satisfying results in the diagnosis of various lesions (14)(15)(16). Since TA can acquire the additional quantified information from the images that are not discernible to the human eye, more studies have been long established in the advantages of TA in facilitating differential diagnosis (17,18).…”

Section: Introductionmentioning

confidence: 99%

MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions

Zhao

Chen²,

Zhang

et al. 2021

Front. Oncol.

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BackgroundDifferential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem.MethodThis current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm.ResultsAll five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group.ConclusionThe evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result.

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Section: Introductionmentioning

confidence: 99%

MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions

Zhao

Chen²,

Zhang

et al. 2021

Front. Oncol.

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“…In addition, pancreatic cystic lesions have been categorized using radiomics methods in multiple studies [19,20]. Although these findings confirmed the feasibility of radiomics for the assessment of pancreatic cystic lesions [21][22][23][24][25][26][27][28], the robustness of these radiomics diagnostic models may be limited due to the relatively small datasets included in most studies. Therefore, the accumulation of additional research data is required for the study of pancreatic cystic lesions.…”

Section: Introductionmentioning

confidence: 93%

Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models

et al. 2022

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Background Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of personalized diagnosis and treatment plans. This study aimed to build radiomics deep learning (DL) models using computed tomography (CT) data for the preoperative differential diagnosis of common cystic tumors of the pancreas. Methods Clinical and CT data of 193 patients with PCN were collected for this study. Among these patients, 99 were pathologically diagnosed with pancreatic serous cystadenoma (SCA), 55 were diagnosed with mucinous cystadenoma (MCA) and 39 were diagnosed with intraductal papillary mucinous neoplasm (IPMN). The regions of interest (ROIs) were obtained based on manual image segmentation of CT slices. The radiomics and radiomics-DL models were constructed using support vector machines (SVMs). Moreover, based on the fusion of clinical and radiological features, the best combined feature set was obtained according to the Akaike information criterion (AIC) analysis. Then the fused model was constructed using logistic regression. Results For the SCA differential diagnosis, the fused model performed the best and obtained an average area under the curve (AUC) of 0.916. It had a best feature set including position, polycystic features (≥6), cystic wall calcification, pancreatic duct dilatation and radiomics-DL score. For the MCA and IPMN differential diagnosis, the fused model with AUC of 0.973 had a best feature set including age, communication with the pancreatic duct and radiomics score. Conclusions The radiomics, radiomics-DL and fused models based on CT images have a favorable differential diagnostic performance for SCA, MCA and IPMN. These findings may be beneficial for the exploration of individualized management strategies.

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“…Thus, varieties of researches including basic and clinical are done over enhancing CRC efficiency. Researchers made a conclusion that CRC patients with MSI-H, high TMB and dMMR are demonstrated to get a nice response to ICIs treatment and own a considerably brilliant prognosis (65)(66)(67)(75)(76)(77). Following, FDA approved pebrolizumab used for the treatment of refractory, metastatic solid tumors with mismatch repair defects, high microsatellite instability, and applied nivolumab approval for the treatment of colorectal cancer with a mismatch repair defect and high microsatellite instability.…”

Section: Icis Treatment For Colorectal Cancermentioning

confidence: 99%

The Role of m6A Epigenetic Modification in the Treatment of Colorectal Cancer Immune Checkpoint Inhibitors

Tong

Smith

et al. 2022

Front. Immunol.

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Tumor immunotherapy, one of the efficient therapies in cancers, has been called to the scientific community’s increasing attention lately. Among them, immune checkpoint inhibitors, providing entirely new modalities to treat cancer by leveraging the patient’s immune system. They are first-line treatments for varieties of advanced malignancy, such as melanoma, gastrointestinal tumor, esophageal cancer. Although immune checkpoint inhibitors (ICIs) treatment has been successful in different cancers, drug resistance and relapses are common, such as in colorectal cancer. Therefore, it is necessary to improve the efficacy of immune checkpoint therapy for cancer patients who do not respond or lowly response to current treatments. N6-methyladenosine (m6A), as a critical regulator of transcript expression, is the most frequently internal modification of mRNA in the human body. Recently, it has been proposed that m6A epigenetic modification is a potential driver of tumor drug resistance. In this report, we will briefly outline the relevant mechanisms, general treatment status of immune checkpoint inhibitors in colorectal cancer, how m6A epigenetic modifications regulate the response of ICIs in CRC and provide new strategies for overcoming the resistance of ICIs in CRC.

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Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods

Cited by 14 publications

References 43 publications

MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions

MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions

Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models

The Role of m6A Epigenetic Modification in the Treatment of Colorectal Cancer Immune Checkpoint Inhibitors

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