Dataset Information

Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer.

ABSTRACT: PURPOSE:To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAFV600E mutation in thyroid cancer. METHODS:469 thyroid cancers in 469 patients were included in this retrospective study. A CAD program recently developed using the deep CNN provided risks of malignancy (0-100%) as well as binary results (cancer or not). Using the CAD program, we calculated the risk of malignancy based on a US image of each thyroid nodule (CAD value). Univariate and multivariate logistic regression analyses were performed including patient demographics, the American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TIRADS) categories and risks of malignancy calculated through CAD to identify independent predictive factors for the BRAFV600E mutation in thyroid cancer. The predictive power of the CAD value and final multivariable model for the BRAFV600E mutation in thyroid cancer were measured using the area under the receiver operating characteristic (ROC) curves. RESULTS:In this study, 380 (81%) patients were positive and 89 (19%) patients were negative for the BRAFV600E mutation. On multivariate analysis, older age (OR = 1.025, p = 0.018), smaller size (OR = 0.963, p = 0.006), and higher CAD value (OR = 1.016, p = 0.004) were significantly associated with the BRAFV600E mutation. The CAD value yielded an AUC of 0.646 (95% CI: 0.576, 0.716) for predicting the BRAFV600E mutation, while the multivariable model yielded an AUC of 0.706 (95% CI: 0.576, 0.716). The multivariable model showed significantly better performance than the CAD value alone (p = 0.004). CONCLUSION:Deep learning-based CAD for thyroid US can help us predict the BRAFV600E mutation in thyroid cancer. More multi-center studies with more cases are needed to further validate our study results.

SUBMITTER: Yoon J

PROVIDER: S-EPMC7688114 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer.

Yoon Jiyoung J Lee Eunjung E Koo Ja Seung JS Yoon Jung Hyun JH Nam Kee-Hyun KH Lee Jandee J Jo Young Suk YS Moon Hee Jung HJ Park Vivian Youngjean VY Kwak Jin Young JY

PloS one 20201125 11

<h4>Purpose</h4>To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAFV600E mutation in thyroid cancer.<h4>Methods</h4>469 thyroid cancers in 469 patients were included in this retrospective study. A CAD program recently developed using the deep CNN provided risks of malignancy (0-100%) as well as binary results (cancer or not). Using the CAD program, we calculated the risk of mal ...[more]

PMID: 33237975

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Project description:ObjectivesThe BRAFV600E mutation is a major driver mutation in papillary thyroid cancer. The aim of this study was to elucidate the correlation between DNA methylation and gene expression changes induced by the BRAFV600E mutation in thyroid cells.MethodsWe used Nthy/BRAF cell lines generated by transfection of Nthy/ori cells with the wild-type BRAF gene (Nthy/WT cells) and the V600E mutant-type BRAF gene (Nthy/V600E cells). We performed gene expression microarray and DNA methylation array analyses for Nthy/WT and Nthy/V600E cells. Two types of array data were integrated to identify inverse correlations between methylation and gene expression. The results were verified in silico using data from The Cancer Genome Atlas (TCGA) and in vivo through pyrosequencing and quantitative real-time polymerase chain reaction (qRT-PCR).ResultsIn the Nthy/V600E cells, 199,821 probes were significantly hypermethylated, and 697 genes showed a "hypermethylation-downregulation" pattern in Nthy/V600E. Tumor suppressor genes and apoptosis-related genes were included. In total, 66,446 probes were significantly hypomethylated, and 227 genes showed a "hypomethylation-upregulation" pattern in Nthy/V600E cells. Protooncogenes and developmental protein-coding genes were included. In the TCGA analysis, 491/697 (70.44%) genes showed a hypermethylation-downregulation pattern, and 153/227 (67.40%) genes showed a hypomethylation-upregulation pattern. Ten selected genes showed a similar methylation-gene expression pattern in pyrosequencing and qRT-PCR.ConclusionInduction of the BRAFV600E mutation in thyroid cells led to frequent hypermethylation. Anticancer genes, such as those involved in tumor suppression or apoptosis, were downregulated by upstream hypermethylation, whereas carcinogenic genes, such as protooncogenes, were upregulated by hypomethylation. Our results suggest that the BRAFV600E mutation in thyroid cells modulates DNA methylation and results in cancer-related gene expression.

Project description:Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91-0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85-0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35-0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92-0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.

Project description:Background Gastric cancer with synchronous distant metastases indicates a dismal prognosis. The success in survival improvement mainly relies on our ability to predict the potential benefit of a therapy. Our objective is to develop an artificial intelligence annotated clinical-pathologic risk model to predict its outcomes. Methods In participants (n=47553) with gastric cancer of the surveillance, epidemiology, and end results program, we selected patients with distant metastases at first diagnosis, complete clinical-pathologic data and follow-up information. Patients were randomly divided into the training and test cohort at 7:3 ratio. 93 patients with advanced gastric cancer from six other cancer centers were collected as the external validation cohort. Multivariable analysis was used to identify the prognosis-related clinical-pathologic features. Then a survival prediction model was established and validated. Importantly, we provided explanations to the prediction with artificial intelligence SHAP (Shapley additive explanations) method. We also provide novel insights into treatment options. Results Data from a total 2549 patients were included in model development and internal test (median age, 61 years [range, 53-69 years]; 1725 [67.7%] male). Data from an additional 93 patients were collected as the external validation cohort (median age, 59 years [range, 48-66 years]; 51 [54.8%] male). The clinical-pathologic model achieved a consistently high accuracy for predicting prognosis in the training (C-index: 0.705 [range, 0.690-0.720]), test (C-index: 0.737 [range, 0.717-0.757]), and external validation (C-index: 0.694 [range, 0.562-0.826]) cohorts. Shapley values indicated that undergoing surgery, chemotherapy, young, absence of lung metastases and well differentiated were the top 5 contributors to the high likelihood of survival. A combination of surgery and chemotherapy had the greatest benefit. However, aggressive treatment did not equate to a survival benefit. SHAP dependence plots demonstrated insightful nonlinear interactive associations among predictors in survival benefit prediction. For example, patients who were elderly, or poor differentiated, or presence of lung or bone metastases had a worse prognosis if they undergo surgery or chemotherapy, while patients with metastases to liver alone seemed to gain benefit from surgery and chemotherapy. Conclusion In this large multicenter cohort study, we developed an artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer. It could be used to discuss treatment options.

Project description:BackgroundPatient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians.ObjectiveTo make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room.MethodWe developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset.ResultsOf the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84-0.93) for detecting patients who required urgent revascularization.ConclusionsOur artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort.

Project description:BACKGROUND:Intranuclear inclusions (NI) in normal and neoplastic tissues have been known for years, representing one of the diagnostic criteria for papillary thyroid carcinoma (PTC). BRAF activation is involved among others in autophagy. NI in hepatocellular carcinoma contain autophagy-associated proteins. Our aim was to clarify if NI in thyroid carcinoma (TC) have a biological function. METHODS:NI in 107 paraffin-embedded specimens of TC including all major subtypes were analyzed. We considered an inclusion as positive if it was delimited by a lamin AC (nuclear membrane marker) stained intact membrane and completely closed. Transmission electron microscopy (TEM), immunohistochemistry (IHC), immunofluorescence (IF) and 3D reconstruction were performed to investigate content and shape of NI; BRAFV600E mutation was analyzed by next generation sequencing. RESULTS:In 29% of the TCs at least one lamin AC positive intranuclear inclusion was detected; most frequently (76%) in PTCs. TEM analyses revealed degenerated organelles and heterolysosomes within such NI; 3D reconstruction of IF stained nuclei confirmed complete closure by the nuclear membrane without any contact to the cytoplasm. NI were positively stained for the autophagy-associated proteins LC3B, ubiquitin, cathepsin D, p62/sequestosome1 and cathepsin B in 14-29% of the cases. Double-IF revealed co-localization of LC3B & ubiquitin, p62 & ubiquitin and LC3B & p62 in the same NI. BRAFV600E mutation, exclusively detected in PTCs, was significantly associated with the number of NI/PTC (p = 0.042) and with immunoreactivity for autophagy-associated proteins in the NI (p≤0.035). BRAF-IHC revealed that some of these BRAF-positive thyrocytes contained mutant BRAF in their NI co-localized with autophagy-associated proteins. CONCLUSIONS:NI are completely delimited by nuclear membrane in TC. The presence of autophagy-associated proteins within the NI together with degenerated organelles and lysosomal proteases suggests their involvement in autophagy and proteolysis. Whether and how BRAFV600E protein is degraded in NI needs further investigation.

Dataset Information

Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer.

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Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer.

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