EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy

• URL: http://arxiv.org/abs/2402.14099v2
• Date: Wed, 31 Jul 2024 21:57:33 GMT
• Title: EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy
• Authors: Hamed Hooshangnejad, Xue Feng, Gaofeng Huang, Rui Zhang, Katelyn Kelly, Quan Chen, Kai Ding,
• Abstract summary: Over 60% of non-small cell lung cancer (NSCLC) patients require radiation therapy. Our approach resulted in a 250% boost in successful nodule detection using the data from ten NSCLC patients treated in our institution.
• Score: 7.531407604292937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lung cancer is a devastating disease with the highest mortality rate among cancer types. Over 60% of non-small cell lung cancer (NSCLC) patients, which accounts for 87% of diagnoses, require radiation therapy. Rapid treatment initiation significantly increases the patient's survival rate and reduces the mortality rate. Accurate tumor segmentation is a critical step in the diagnosis and treatment of NSCLC. Manual segmentation is time and labor-consuming and causes delays in treatment initiation. Although many lung nodule detection methods, including deep learning-based models, have been proposed, there is still a long-standing problem of high false positives (FPs) with most of these methods. Here, we developed an electronic health record (EHR) guided lung tumor auto-segmentation called EXACT-Net (EHR-enhanced eXACtitude in Tumor segmentation), where the extracted information from EHRs using a pre-trained large language model (LLM), was used to remove the FPs and keep the TP nodules only. The auto-segmentation model was trained on NSCLC patients' computed tomography (CT), and the pre-trained LLM was used with the zero-shot learning approach. Our approach resulted in a 250% boost in successful nodule detection using the data from ten NSCLC patients treated in our institution.

Related papers

• Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging [65.83291923029985]
  Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment PCa lesions from a 200 CDI$
  arXiv  Detail & Related papers  (2025-01-15T22:23:41Z)
• Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning [0.0]
  This study introduces an innovative approach to lung nodule segmentation by utilizing the Segment Anything Model (SAM) combined with transfer learning techniques. The proposed method leverages Bounding Box prompts and a vision transformer model to enhance segmentation performance, achieving high accuracy, Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) metrics. The findings demonstrate the proposed model effectiveness in precisely segmenting lung nodules from CT scans, underscoring its potential to advance early detection and improve patient care outcomes in lung cancer diagnosis.
  arXiv  Detail & Related papers  (2024-12-31T18:21:57Z)
• Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports [68.39938936308023]
  We propose a novel text-guided learning method to achieve highly accurate cancer detection results. Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability.
  arXiv  Detail & Related papers  (2024-05-23T07:03:38Z)
• Parameter-Efficient Methods for Metastases Detection from Clinical Notes [19.540079966780954]
  The objective of this study is to automate the detection of metastatic liver disease from free-style computed tomography (CT) radiology reports. Our research demonstrates that transferring knowledge using three approaches can improve model performance.
  arXiv  Detail & Related papers  (2023-10-27T20:30:59Z)
• Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients [60.78505216352878]
  We propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total.
  arXiv  Detail & Related papers  (2022-11-08T11:50:31Z)
• Lung nodules segmentation from CT with DeepHealth toolkit [6.980270783615686]
  The goal of this study was to demonstrate the feasibility of Deephealth toolkit including PyECVL and PyEDDL libraries to precisely segment lung nodules. The results depict accurate segmentation of lung nodules across a wide diameter range and better accuracy over a traditional detection approach.
  arXiv  Detail & Related papers  (2022-08-01T06:54:12Z)
• Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning [2.744770849264355]
  Lung cancer screening (LCS) using annual low-dose computed tomography (CT) scanning has been proven to significantly reduce lung cancer mortality. Improving risk stratification of malignancy risk in lung nodules can be enhanced using machine/deep learning algorithms. Here we show the performance of our time-series deep learning model (DeepCAD-NLM-L) which integrates multi-model information across three longitudinal data domains.
  arXiv  Detail & Related papers  (2022-03-30T18:40:36Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images [9.525711971667679]
  Lung cancer is the leading cause of cancer death worldwide. Deep learning techniques are effective at automatically diagnosing lung cancer. These techniques have yet to be clinically approved and adopted by the medical community.
  arXiv  Detail & Related papers  (2020-12-10T04:11:59Z)
• 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)

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.