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
• 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

• Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers [0.0]
  Early identification of individuals at elevated risk of Chlamydia trachomatis infection may enable optimal use of molecular testing in resource-aware screening.<n>We evaluate the feasibility of pre-test risk stratification using machine-learning models trained on routinely available, non-invasive clinical data.
  arXiv  Detail & Related papers  (2026-05-09T19:21:59Z)
• Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma [5.924316400347799]
  We propose a 2.5D multimodal deep learning framework that processes lesion-dense omental slices using a pre-trained Vision Transformer encoder.<n>Our model, integrating imaging and clinical data, achieved a ROC-AUC of 0.95 alongside 95% accuracy and 80% precision on the internal test cohort.<n>These preliminary results demonstrate the feasibility of transformer-based deep learning for preoperative prediction of CRS in HGSOC.
  arXiv  Detail & Related papers  (2026-04-10T10:33:07Z)
• Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering [94.37535002230504]
  We develop a training-free, inference-time control framework termed Semantically Decoupled Latent Steering.<n>Our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition.<n>We show that our approach significantly reduces the probability of historical hallucinations.
  arXiv  Detail & Related papers  (2026-02-27T04:49:01Z)
• Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma [0.9165796164519936]
  Neoadjuvant chemotherapy (NACT) is often used to reduce tumor burden before surgery, but about 40% of patients show limited response.<n>Radiomics, combined with machine learning (ML), offers a promising non-invasive method for predicting NACT response.<n>This study aimed to improve response prediction in HGSOC patients undergoing NACT by integration different feature selection methods.
  arXiv  Detail & Related papers  (2026-01-13T11:29:02Z)
• Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images [2.23127246021293]
  Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies.<n>We present an attention-based multiple instance learning framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides.
  arXiv  Detail & Related papers  (2025-05-20T02:06:34Z)
• Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning [0.5116003548817487]
  Lung cancer is the primary cause of cancer death globally, with non-small cell lung cancer (NSCLC) emerging as its most prevalent subtype. Osimertinib, a third-generation EGFR-tyrosine kinase inhibitor (TKI), has demonstrated remarkable efficacy in the treatment of NSCLC patients. Despite its established efficacy, drug resistance poses a significant challenge for patients to fully benefit from osimertinib.
  arXiv  Detail & Related papers  (2025-03-31T14:47:02Z)
• Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes [37.440449828136586]
  Investigation focuses on a novel approach termed DE-ViT.<n>This methodology is employed in a Few-Shot paradigm, wherein training relies on a limited number of images.<n>While DE-ViT has demonstrated state-of-the-art performance on the COCO and LVIS datasets, both baseline models surpassed its performance on the Raabin-WBC dataset.
  arXiv  Detail & Related papers  (2025-03-21T12:46:49Z)
• Semi-Supervised Learning for Dose Prediction in Targeted Radionuclide: A Synthetic Data Study [42.75913504373651]
  Targeted Radionuclide Therapy (TRT) is a modern strategy in radiation oncology that aims to administer a potent radiation dose specifically to cancer cells.<n>Deep learning holds promise for personalizing TRT doses.<n>Current methods require large time series of SPECT imaging, which is hardly achievable in routine clinical practice.
  arXiv  Detail & Related papers  (2025-03-07T12:21:09Z)
• CardioLab: Laboratory Values Estimation and Monitoring from Electrocardiogram Signals -- A Multimodal Deep Learning Approach [1.068128849363198]
  We utilize MIMIC-IV dataset to develop multimodal deep-learning models to demonstrate the feasibility of estimating (real-time) and monitoring (predict at future intervals) laboratory value abnormalities. The models exhibit a strong predictive performance with AUROC scores above 0.70 in a statistically significant manner for 23 laboratory values in the estimation setting and up to 26 values in the monitoring setting.
  arXiv  Detail & Related papers  (2024-11-22T12:10:03Z)
• 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)
• Neural Network-Based Histologic Remission Prediction In Ulcerative Colitis [38.150634108667774]
  Histologic remission is a new therapeutic target in ulcerative colitis (UC) Endocytoscopy (EC) is a novel ultra-high magnification endoscopic technique. We propose a neural network model that can assess histological disease activity in EC images.
  arXiv  Detail & Related papers  (2023-08-28T15:54:14Z)
• 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)
• Foresight -- Deep Generative Modelling of Patient Timelines using Electronic Health Records [46.024501445093755]
  Temporal modelling of medical history can be used to forecast and simulate future events, estimate risk, suggest alternative diagnoses or forecast complications. We present Foresight, a novel GPT3-based pipeline that uses NER+L tools (i.e. MedCAT) to convert document text into structured, coded concepts.
  arXiv  Detail & Related papers  (2022-12-13T19:06:00Z)
• 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)
• Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation [44.02622933605018]
  Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM) We aimed to compare nine machine learning classifiers to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,
  arXiv  Detail & Related papers  (2021-02-10T15:10:37Z)
• Predicting Clinical Trial Results by Implicit Evidence Integration [40.80948875051806]
  We introduce a novel Clinical Trial Result Prediction (CTRP) task. In the CTRP framework, a model takes a PICO-formatted clinical trial proposal with its background as input and predicts the result. We exploit large-scale unstructured sentences from medical literature that implicitly contain PICOs and results as evidence.
  arXiv  Detail & Related papers  (2020-10-12T12:25:41Z)
• 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.