Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

• URL: http://arxiv.org/abs/2307.04525v2
• Date: Sun, 16 Jul 2023 03:06:24 GMT
• Title: Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
• Authors: Mingze Yuan, Yingda Xia, Xin Chen, Jiawen Yao, Junli Wang, Mingyan Qiu, Hexin Dong, Jingren Zhou, Bin Dong, Le Lu, Li Zhang, Zaiyi Liu, Ling Zhang
• Abstract summary: Gastric cancer is the third leading cause of cancer-related mortality worldwide. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner.
• Score: 38.46196471197819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on non-contrast CT scans for gastric cancer detection. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner. Our model incorporates learnable clusters that encode the texture and shape prototypes of gastric cancer, utilizing self- and cross-attention to interact with convolutional features. In our experiments, the proposed method achieves a sensitivity of 85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal. In comparison, two radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We also obtain a specificity of 97.7% on an external test set with 903 normal cases. Our approach performs comparably to established state-of-the-art gastric cancer screening tools like blood testing and endoscopy, while also being more sensitive in detecting early-stage cancer. This demonstrates the potential of our approach as a novel, non-invasive, low-cost, and accurate method for opportunistic gastric cancer screening.

Related papers

• 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)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
  The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
  arXiv  Detail & Related papers  (2023-04-12T15:08:34Z)
• Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features [6.132193527180974]
  Pancreatic cancer is one of the global leading causes of cancer-related deaths. We propose a method for detecting pancreatic tumor that utilizes clinically-relevant features in the surrounding anatomical structures.
  arXiv  Detail & Related papers  (2022-08-06T20:38:25Z)
• A Combined PCA-MLP Network for Early Breast Cancer Detection [0.0]
  We have studied different machine learning algorithms to detect whether a patient is likely to face breast cancer or not. Our 4 layers-PCA network has obtained the best accuracy of 100% with a mean of 90.48% on the BCCD dataset.
  arXiv  Detail & Related papers  (2022-06-18T06:17:40Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Noncoding RNAs and deep learning neural network discriminate multi-cancer types [0.0]
  We develop a comprehensive detection system to classify all common cancer types. Our system can accurately detect cancer vs healthy object with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve) A comprehensive marker panel can simultaneously multi-classify all common cancers with a stable 78% of accuracy at heterological cancerous tissues and conditions.
  arXiv  Detail & Related papers  (2021-03-01T18:20:45Z)
• Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors [72.65802386845002]
  This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs) in abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. Our approach outperforms state-of-the-art segmentation networks and achieves a sensitivity of $89.47%$ at a specificity of $81.08%$.
  arXiv  Detail & Related papers  (2020-04-04T21:21:44Z)
• Predicting the risk of pancreatic cancer with a CT-based ensemble AI algorithm [0.0]
  Pancreatic cancer is a lethal disease, hard to diagnose and results in poor prognosis and high mortality. We propose an ensemble AI algorithm to predict universally cancer risk of all kinds of pancreatic lesions with noncontrast CT.
  arXiv  Detail & Related papers  (2020-04-03T06:06:43Z)

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.