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نتیجه جستجو - رادیوژنومیک

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Deep learning for identifying radiogenomic associations in breast cancer
یادگیری عمیق برای شناسایی انجمنهای رادیوژنومیک در سرطان پستان-2019
Rationale and objectives: To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and methods: In this institutional review board–approved single-center study, we analyzed DCE-MR images of 270 patients at our institution. Lesions of interest were identified by radiologists. The task was to automatically determine whether the tumor is of the Luminal A subtype or of another subtype based on the MR image patches representing the tumor. Three different deep learning approaches were used to classify the tumor according to their molecular subtypes: learning from scratch where only tumor patches were used for training, transfer learning where networks pre-trained on natural images were fine-tuned using tumor patches, and offthe- shelf deep features where the features extracted by neural networks trained on natural images were used for classification with a support vector machine. Network architectures utilized in our experiments were GoogleNet, VGG, and CIFAR. We used 10-fold crossvalidation method for validation and area under the receiver operating characteristic (AUC) as the measure of performance. Results: The best AUC performance for distinguishing molecular subtypes was 0.65 (95% CI:[0.57,0.71]) and was achieved by the off-the-shelf deep features approach. The highest AUC performance for training from scratch was 0.58 (95% CI:[0.51,0.64]) and the best AUC performance for transfer learning was 0.60 (95% CI: [0.52,0.65]) respectively. For the off-the-shelf approach, the features extracted from the fully connected layer performed the best. Conclusion: Deep learning may play a role in discovering radiogenomic associations in breast cancer.
Keywords: Deep learning | Radiogenomic | Breast cancer subtype
مقاله انگلیسی
2 Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
انجمن زیر نوع ژنومی گلیوم درجه پایین با ویژگی های شکل خودکار توسط یک الگوریتم یادگیری عمیق استخراج شده-2019
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes. We used preoperative imaging and genomic data of 110 patients from 5 institutions with lower-grade gliomas from The Cancer Genome Atlas. Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors. Genomic data for the analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/ 19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. To analyze the relationship between the imaging features and genomic clusters, we conducted the Fisher exact test for 10 hypotheses for each pair of imaging feature and genomic subtype. To account for multiple hypothesis testing, we applied a Bonferroni correction. P-values lower than 0.005 were considered statistically significant. We found the strongest association between RNASeq clusters and the bounding ellipsoid volume ratio (p < 0.0002) and between RNASeq clusters and margin fluctuation (p < 0.005). In addition, we identified associations between bounding ellipsoid volume ratio and all tested molecular subtypes (p < 0.02) as well as between angular standard deviation and RNASeq cluster (p < 0.02). In terms of automatic tumor segmentation that was used to generate the quantitative image characteristics, our deep learning algorithm achieved a mean Dice coefficient of 82% which is comparable to human performance.
Keywords: Deep learning | Brain segmentation | Radiogenomics | MRI | LGG
مقاله انگلیسی
3 CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach
تجزیه و تحلیل بافت CT برای پیش بینی وضعیت جهش KRAS در سرطان کولورکتال از طریق یک روش یادگیری ماشین-2019
Purpose: This study aimed to investigate whether a machine learning-based computed tomography (CT) texture analysis could predict the mutation status of V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) in colorectal cancer. Method: This retrospective study comprised 40 patients with pathologically confirmed colorectal cancer who underwent KRAS mutation testing, contrast-enhancement CT, and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) before treatment. Of the 40 patients, 20 had mutated KRAS genes, whereas 20 had wild-type KRAS genes. Fourteen CT texture parameters were extracted from portal venous phase CT images of primary tumors, and the maximum standard uptake values (SUVmax) on 18F-FDG PET images were recorded. Univariate logistic regression was used to develop predictive models for each CT texture parameter and SUVmax, and a machine learning method (multivariate support vector machine) was used to develop a comprehensive set of CT texture parameters. The area under the receiver operating characteristic (ROC) curve (AUC) of each model was calculated using five-fold cross validation. In addition, the performance of the machine learning method with the CT texture parameters was compared with that of SUVmax. Results: In the univariate analyses, the AUC of each CT texture parameter ranged from 0.4 to 0.7, while the AUC of the SUVmax was 0.58. Comparatively, the multivariate support vector machine with comprehensive CT texture parameters yielded an AUC of 0.82, indicating a superior prediction performance when compared to the SUVmax. Conclusions: A machine learning-based CT texture analysis was superior to the SUVmax for predicting the KRAS mutation status of a colorectal cancer.
Keywords: Colorectal cancer | CT texture analysis | Machine learning | KRAS mutation | Radiogenomics
مقاله انگلیسی
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