Prediction of MET Overexpression in Non-Small Cell Lung Adenocarcinomas
from Hematoxylin and Eosin Images
- URL: http://arxiv.org/abs/2310.07682v2
- Date: Thu, 12 Oct 2023 22:28:05 GMT
- Title: Prediction of MET Overexpression in Non-Small Cell Lung Adenocarcinomas
from Hematoxylin and Eosin Images
- Authors: Kshitij Ingale, Sun Hae Hong, Josh S.K. Bell, Abbas Rizvi, Amy Welch,
Lingdao Sha, Irvin Ho, Kunal Nagpal, Aicha BenTaieb, Rohan P Joshi, Martin C
Stumpe
- Abstract summary: MET protein overexpression is a targetable event in non-small cell lung cancer (NSCLC)
Development of pre-screening algorithms using digitized hematoxylin and eosin (H&E)-stained slides to predict MET overexpression could promote testing for those who will benefit most.
- Score: 0.4306805601880342
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: MET protein overexpression is a targetable event in non-small cell lung
cancer (NSCLC) and is the subject of active drug development. Challenges in
identifying patients for these therapies include lack of access to validated
testing, such as standardized immunohistochemistry (IHC) assessment, and
consumption of valuable tissue for a single gene/protein assay. Development of
pre-screening algorithms using routinely available digitized hematoxylin and
eosin (H&E)-stained slides to predict MET overexpression could promote testing
for those who will benefit most. While assessment of MET expression using IHC
is currently not routinely performed in NSCLC, next-generation sequencing is
common and in some cases includes RNA expression panel testing. In this work,
we leveraged a large database of matched H&E slides and RNA expression data to
train a weakly supervised model to predict MET RNA overexpression directly from
H&E images. This model was evaluated on an independent holdout test set of 300
over-expressed and 289 normal patients, demonstrating an ROC-AUC of 0.70 (95th
percentile interval: 0.66 - 0.74) with stable performance characteristics
across different patient clinical variables and robust to synthetic noise on
the test set. These results suggest that H&E-based predictive models could be
useful to prioritize patients for confirmatory testing of MET protein or MET
gene expression status.
Related papers
- Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial [0.6087644423424302]
We present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides.
Our model produces a visual vascular network which is the basis of the model's prediction.
Our approach offers insights into angiogenesis biology and AA treatment response.
arXiv Detail & Related papers (2024-05-28T16:21:20Z) - Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals.
Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
arXiv Detail & Related papers (2024-04-26T16:39:50Z) - Prediction of Rapid Early Progression and Survival Risk with
Pre-Radiation MRI in WHO Grade 4 Glioma Patients [0.5365740459403827]
We investigate the potential of ra-diomics, sophisticated multi-resolution fractal texture features, and different molecular features as a diagnostic and prognostic tool for prediction of REP from non-REP cases.
The prediction of survival for the patients cohort produces precision of 0.881 with standard deviation of 0.056.
The experimental result further shows that mul-ti-resolution fractal texture features perform better than conventional radiomics features for REP and survival outcomes.
arXiv Detail & Related papers (2023-06-28T20:03:18Z) - hist2RNA: An efficient deep learning architecture to predict gene
expression from breast cancer histopathology images [11.822321981275232]
Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively.
We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA-sequencing techniques to predict the expression of 138 genes.
arXiv Detail & Related papers (2023-04-10T10:54:32Z) - Learning to diagnose cirrhosis from radiological and histological labels
with joint self and weakly-supervised pretraining strategies [62.840338941861134]
We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset.
We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis.
This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
arXiv Detail & Related papers (2023-02-16T17:06:23Z) - Predicting Molecular Phenotypes with Single Cell RNA Sequencing Data: an
Assessment of Unsupervised Machine Learning Models [0.0]
This study is to evaluate unsupervised machine learning on classifying treatment-resistant phenotypes in heterogeneous tumors.
scRNAseq quantifies mRNA in cells and characterizes cell phenotypes.
clusters generated from this pipeline can be used to understand cancer cell behavior and malignant growth.
arXiv Detail & Related papers (2021-08-11T05:30:37Z) - Transcriptome-wide prediction of prostate cancer gene expression from
histopathology images using co-expression based convolutional neural networks [0.8874479658912061]
We propose a new, computationally efficient approach for disease specific modelling of relationships between morphology and gene expression.
We conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to predict bulk RNA-sequencing estimates.
arXiv Detail & Related papers (2021-04-19T13:50:25Z) - MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
This paper aims at a unified deep learning approach to predict patient prognosis and therapy response.
We formalize the prognosis modeling as a multi-modal asynchronous time series classification task.
Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
arXiv Detail & Related papers (2020-10-08T15:30:17Z) - Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission.
Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
arXiv Detail & Related papers (2020-07-20T19:08:50Z) - Hemogram Data as a Tool for Decision-making in COVID-19 Management:
Applications to Resource Scarcity Scenarios [62.997667081978825]
COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure.
This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients.
Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
arXiv Detail & Related papers (2020-05-10T01:45:03Z) - Short Term Blood Glucose Prediction based on Continuous Glucose
Monitoring Data [53.01543207478818]
This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools.
We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
arXiv Detail & Related papers (2020-02-06T16:39:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.