Lymphoid Infiltration Assessment of the Tumor Margins in H&E Slides
- URL: http://arxiv.org/abs/2407.16464v1
- Date: Tue, 23 Jul 2024 13:27:44 GMT
- Title: Lymphoid Infiltration Assessment of the Tumor Margins in H&E Slides
- Authors: Zhuxian Guo, Amine Marzouki, Jean-François Emile, Henning Müller, Camille Kurtz, Nicolas Loménie,
- Abstract summary: Lymphoid infiltration at tumor margins is a key prognostic marker in solid tumors.
Current assessment methods, heavily reliant onchemistry, face challenges in tumor margin delineation.
We propose a Hematoxylin and Eosin (H&E) staining-based approach, underpinned by an advanced lymphocyte segmentation model.
- Score: 1.715270928578365
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Lymphoid infiltration at tumor margins is a key prognostic marker in solid tumors, playing a crucial role in guiding immunotherapy decisions. Current assessment methods, heavily reliant on immunohistochemistry (IHC), face challenges in tumor margin delineation and are affected by tissue preservation conditions. In contrast, we propose a Hematoxylin and Eosin (H&E) staining-based approach, underpinned by an advanced lymphocyte segmentation model trained on a public dataset for the precise detection of CD3+ and CD20+ lymphocytes. In our colorectal cancer study, we demonstrate that our H&E-based method offers a compelling alternative to traditional IHC, achieving comparable results in many cases. Our method's validity is further explored through a Turing test, involving blinded assessments by a pathologist of anonymized curves from H&E and IHC slides. This approach invites the medical community to consider Turing tests as a standard for evaluating medical applications involving expert human evaluation, thereby opening new avenues for enhancing cancer management and immunotherapy planning.
Related papers
- Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer.
The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts.
This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
arXiv Detail & Related papers (2024-05-13T15:40:56Z) - Non-Linear Self Augmentation Deep Pipeline for Cancer Treatment outcome
Prediction [7.455416595124159]
Authors present an innovative strategy that harnesses a non-linear cellular architecture in conjunction with a deep downstream classifier.
This approach aims to carefully select and enhance 2D features extracted from chest-abdomen CT images, thereby improving the prediction of treatment outcomes.
The proposed pipeline has been meticulously designed to seamlessly integrate with an advanced embedded Point of Care system.
arXiv Detail & Related papers (2023-07-26T15:01:26Z) - Experts' cognition-driven safe noisy labels learning for precise
segmentation of residual tumor in breast cancer [5.445090025094291]
We propose an experts' cognition-driven safe noisy labels learning (ECDSNLL) approach.
ECDSNLL is constructed by integrating the experts' cognition about identifying residual tumor in breast cancer and the artificial intelligence experts' cognition about data modeling.
We show the advantages of the proposed ECDSNLL approach and its promising potentials in addressing PSRTBC.
arXiv Detail & Related papers (2023-04-13T03:46:40Z) - 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) - LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset [16.265482903238492]
We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China)
The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in histological images of colon, breast, and prostate cancer stained with CD3 and CD8chemistry.
After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform.
arXiv Detail & Related papers (2023-01-16T08:18:57Z) - Enhancing Clinical Support for Breast Cancer with Deep Learning Models
using Synthetic Correlated Diffusion Imaging [66.63200823918429]
We investigate enhancing clinical support for breast cancer with deep learning models.
We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort.
We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
arXiv Detail & Related papers (2022-11-10T03:02:12Z) - 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) - 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) - An Attention-based Weakly Supervised framework for Spitzoid Melanocytic
Lesion Diagnosis in WSI [1.0948946179065253]
Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer.
The gold standard for its diagnosis and prognosis is the analysis of skin biopsies.
We propose a novel end-to-end weakly-supervised deep learning model, based on inductive transfer learning with an improved convolutional neural network (CNN)
The framework is composed of a source model in charge of finding the tumor patch-level patterns, and a target model focuses on the specific diagnosis of a biopsy.
arXiv Detail & Related papers (2021-04-20T10:18:57Z) - A digital score of tumour-associated stroma infiltrating lymphocytes
predicts survival in head and neck squamous cell carcinoma [1.116655705522709]
infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival.
A deep learning based automated method was employed to segment tumour, stroma and lymphocytes.
The spatial patterns of lymphocytes and tumour-associated stroma were digitally quantified to compute the TASIL-score.
arXiv Detail & Related papers (2021-04-16T19:45:00Z) - Spatio-spectral deep learning methods for in-vivo hyperspectral
laryngeal cancer detection [49.32653090178743]
Early detection of head and neck tumors is crucial for patient survival.
Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors.
We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
arXiv Detail & Related papers (2020-04-21T17:07:18Z)
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.