22,948 research outputs found
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule
classification focusing on (i) usefulness of gradient tree boosting (XGBoost)
and (ii) effectiveness of parameter optimization using Bayesian optimization
(Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung
cancers and 37 benign lung nodules) were included from public databases of CT
images. A variant of local binary pattern was used for calculating feature
vectors. Support vector machine (SVM) or XGBoost was trained using the feature
vectors and their labels. TPE or random search was used for parameter
optimization of SVM and XGBoost. Leave-one-out cross-validation was used for
optimizing and evaluating the performance of our CADx system. Performance was
evaluated using area under the curve (AUC) of receiver operating characteristic
analysis. AUC was calculated 10 times, and its average was obtained. The best
averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were
obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters
for achieving high AUC were obtained with fewer numbers of trials when using
TPE, compared with random search. In conclusion, XGBoost was better than SVM
for classifying lung nodules. TPE was more efficient than random search for
parameter optimization.Comment: 29 pages, 4 figure
Predicting drug response of tumors from integrated genomic profiles by deep neural networks
The study of high-throughput genomic profiles from a pharmacogenomics
viewpoint has provided unprecedented insights into the oncogenic features
modulating drug response. A recent screening of ~1,000 cancer cell lines to a
collection of anti-cancer drugs illuminated the link between genotypes and
vulnerability. However, due to essential differences between cell lines and
tumors, the translation into predicting drug response in tumors remains
challenging. Here we proposed a DNN model to predict drug response based on
mutation and expression profiles of a cancer cell or a tumor. The model
contains a mutation and an expression encoders pre-trained using a large
pan-cancer dataset to abstract core representations of high-dimension data,
followed by a drug response predictor network. Given a pair of mutation and
expression profiles, the model predicts IC50 values of 265 drugs. We trained
and tested the model on a dataset of 622 cancer cell lines and achieved an
overall prediction performance of mean squared error at 1.96 (log-scale IC50
values). The performance was superior in prediction error or stability than two
classical methods and four analog DNNs of our model. We then applied the model
to predict drug response of 9,059 tumors of 33 cancer types. The model
predicted both known, including EGFR inhibitors in non-small cell lung cancer
and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive
analysis further revealed the molecular mechanisms underlying the resistance to
a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer
potential of a novel agent, CX-5461, in treating gliomas and hematopoietic
malignancies. Overall, our model and findings improve the prediction of drug
response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on
Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA.
Currently under consideration for publication in a Supplement Issue of BMC
Genomic
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Pan-cancer classifications of tumor histological images using deep learning
Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNNFirst author draf
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