An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer

• URL: http://arxiv.org/abs/2311.06185v2
• Date: Tue, 21 Nov 2023 17:42:42 GMT
• Title: An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer
• Authors: Adam J Shephard, Mostafa Jahanifar, Ruoyu Wang, Muhammad Dawood, Simon Graham, Kastytis Sidlauskas, Syed Ali Khurram, Nasir M Rajpoot, Shan E Ahmed Raza
• Abstract summary: Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast cancer. We introduce an innovative deep learning pipeline based on the Efficient-UNet architecture to predict the TILs score for breast cancer whole-slide images.
• Score: 10.595165443979857
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast cancer. In this study, we introduce an innovative deep learning pipeline based on the Efficient-UNet architecture to predict the TILs score for breast cancer whole-slide images (WSIs). We first segment tumour and stromal regions in order to compute a tumour bulk mask. We then detect TILs within the tumour-associated stroma, generating a TILs score by closely mirroring the pathologist's workflow. Our method exhibits state-of-the-art performance in segmenting tumour/stroma areas and TILs detection, as demonstrated by internal cross-validation on the TiGER Challenge training dataset and evaluation on the final leaderboards. Additionally, our TILs score proves competitive in predicting survival outcomes within the same challenge, underscoring the clinical relevance and potential of our automated TILs scoring pipeline as a breast cancer prognostic tool.

Related papers

• Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline [0.0]
  We built an end-to-end tumor-infiltrating lymphocytes (TILs) assessment pipeline within QuPath. We applied a pre-trained StarDist deep learning model in QuPath for cell detection and used the extracted cell features to train a binary classifier. Our pipeline was evaluated against pathologist-assigned TIL scores, achieving a Cohen's kappa of 0.71 on an external test set.
  arXiv  Detail & Related papers  (2025-04-23T17:54:59Z)
• 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)
• Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes [0.0]
  This study focuses on breast cancer screening with only a small set of image patches from any sentinel lymph node, positive or negative for metastasis. We design a convolutional neural network in the Python language to build a diagnostic model for this purpose.
  arXiv  Detail & Related papers  (2023-01-14T15:57:00Z)
• A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology [62.997667081978825]
  Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
  arXiv  Detail & Related papers  (2022-09-27T14:08:19Z)
• Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images [12.885308856495353]
  In PET/CT image assessment, automatic tumor segmentation is an important step. Existing methods tend to over-segment the tumor regions and include regions such as the normal high organs, inflammation, and other infections. We introduce a false positive reduction network to overcome this limitation.
  arXiv  Detail & Related papers  (2022-09-16T04:01:14Z)
• Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging [62.346649719614]
  preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment. We present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
  arXiv  Detail & Related papers  (2022-09-13T07:10:14Z)
• AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier [15.924886815041774]
  AutoPET challenge provides a public data set with FDG-PET/CT scans from 900 patients. Our solution achieves a Dice score of 72.12% on patients diagnosed with lung cancer, melanoma, and lymphoma.
  arXiv  Detail & Related papers  (2022-09-02T15:20:28Z)
• Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network [0.0]
  This study aims to classify different types of breast cancer using histological images (HIs) We present an enhanced capsule network that extracts multi-scale features using the Res2Net block and four additional convolutional layers. As a result, the new method outperforms the old ones since it automatically learns the best possible features.
  arXiv  Detail & Related papers  (2022-08-01T03:45:36Z)
• TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge [5.336931842559574]
  The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge aims to assess the prognostic significance of computer-generated TILs scores for predicting survival. We have developed an algorithm to first segment tumor vs. stroma, before localising the tumor bulk region for TILs detection. On preliminary testing, our approach achieved a tumor-stroma weighted Dice score of 0.791 and a FROC score of 0.572 for lymphocytic detection.
  arXiv  Detail & Related papers  (2022-06-23T18:53:24Z)
• 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)
• 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)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

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.