scholarly journals DeepCC: a novel deep learning-based framework for cancer molecular subtype classification

Oncogenesis ◽  
2019 ◽  
Vol 8 (9) ◽  
Author(s):  
Feng Gao ◽  
Wei Wang ◽  
Miaomiao Tan ◽  
Lina Zhu ◽  
Yuchen Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Molecular Subtype ◽  
Subtype Classification

scholarly journals NIMG-08. PREDICTION OF LOWER-GRADE GLIOMA MOLECULAR SUBTYPES USING DEEP LEARNING

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii148-ii148
Author(s):  
Yoshihiro Muragaki ◽  
Yutaka Matsui ◽  
Takashi Maruyama ◽  
Masayuki Nitta ◽  
Taiichi Saito ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Molecular Subtype ◽  
Learning Model ◽  
Group Classification ◽  
Training Dataset ◽  
Lower Grade ◽  
Test Dataset ◽  
Ct Data ◽  
Deep Learning Model

Abstract INTRODUCTION It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively. METHODS A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients. RESULTS The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly. CONCLUSIONS AND FUTURE PERSPECTIVE A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype. We plan to apply the techniques of heat map and/or segmentation for an increase in prediction accuracy.

NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi139-vi139
Author(s):  
Jan Lost ◽  
Tej Verma ◽  
Niklas Tillmanns ◽  
W R Brim ◽  
Harry Subramanian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Prediction Accuracy ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Tumor Resection ◽  
Support Vector ◽  
Hospital Data

Abstract PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.

scholarly journals DNA methylation data-based molecular subtype classification and prediction in patients with gastric cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Qixin Lian ◽  
Bo Wang ◽  
Lijun Fan ◽  
Junqiang Sun ◽  
Guilai Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Molecular Subtype ◽  
Methylation Data ◽  
Subtype Classification

Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides

2020 ◽  
Vol 78 (2) ◽  
pp. 256-264 ◽  
Author(s):  
Ann-Christin Woerl ◽  
Markus Eckstein ◽  
Josephine Geiger ◽  
Daniel C. Wagner ◽  
Tamas Daher ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Deep Learning ◽  
Molecular Subtype ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive

scholarly journals Consensus molecular subtype classification of colorectal adenomas

2018 ◽  
Vol 246 (3) ◽  
pp. 266-276 ◽  
Author(s):  
Malgorzata A Komor ◽  
Linda JW Bosch ◽  
Gergana Bounova ◽  
Anne S Bolijn ◽  
Pien M Delis-van Diemen ◽  
...  
Keyword(s):  
Molecular Subtype ◽  
Colorectal Adenomas ◽  
Subtype Classification ◽  
Consensus Molecular Subtype

Exploring the genomic landscape of hepatobiliary cancers to establish a novel molecular subtype classification.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 562-562
Author(s):  
Anthony Joseph Scholer ◽  
Mary Garland-Kledzik ◽  
Debopyria Ghosh ◽  
Juan Santamaria-Barria ◽  
Adam Khader ◽  
...  
Keyword(s):  
Information Exchange ◽  
Molecular Subtype ◽  
Gene Mutations ◽  
Missense Variant ◽  
Multidimensional Analysis ◽  
Upstream Gene ◽  
Subtype Classification ◽  
Hepatobiliary Cancer ◽  
Genomic Landscape ◽  
Molecular Phenotypes

562 Background: The current understanding of the genomic landscape of hepatobiliary cancer (HBC) is limited. Recent genomic and epigenomic studies have demonstrated that various cancers of different tissue origins can have similar molecular phenotypes. Therefore, the aim of this study is to evaluate the genomic alterations of HBCs as a first step towards creating a novel molecular subtype classification. Methods: A multidimensional analysis of next-generation sequencing for the genomic landscape of HBCs was conducted using mutational data from the AACR-Genomics Evidence Neoplasia Information Exchange database (v. 5.0). From 61 gene mutation platforms, we found 42 genes common to all HBC cases. Associations between histomolecular characteristics of HBCs (hepatocellular (HCC), cholangiocarcinoma (CCA), and gallbladder carcinoma (GBC)) with gene mutations (classified by COSMIC CENSUS) were analyzed using Pearson’s χ2 test. Results: A total of 1,017 alterations were identified in 61 genes (516 missense variant, 157 gene amplifications, 101 inactivating mutations, 106 truncating mutations, 84 upstream gene variants, 37 gene homozygous deletions, 16 gene rearrangements) in 329 patients: 115 (35%) CCA, 87 (26.4%) GBC, and 127 (38.6%) HCC. The majority 77.8% (256) of tumors harbored at least two mutations and 38.9% (128) had at least one alteration, with GBC having a higher average number of alterations (3.28) than HCC (3.23) and CCA (2.49) However, HCCs had the higher maximum number of alterations compared to CCA and GBC (p < 0.05). The ten genes most frequently altered across all the HBCs were TP53, TERT, CTNNB1, KRAS, ARID1A, CDKN2A, IDH1, PIK3CA, MYC, and SMAD4 with disparities in the distribution of genes altered repeatedly observed (p < 0.001). IDH1 mutations were associated with CCA, CTNNB1 and TERT mutations with HCC, and TP53 mutations with both HCC and GBC. Conclusions: HBC subtypes appear to have unique mutational landscapes, but also significant overlap of genetic signatures. Therefore, further exploratory genetic and epigenomic research is needed to develop a histomolecular classification algorithm that can be used for prognostic and therapeutic stratification of these cancers.

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