Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation

• URL: http://arxiv.org/abs/2502.04083v1
• Date: Thu, 06 Feb 2025 13:51:28 GMT
• Title: Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation
• Authors: Tewele W. Tareke, Neree Payan, Alexandre Cochet, Laurent Arnould, Benoit Presles, Jean-Marc Vrigneaud, Fabrice Meriaudeau, Alain Lalande,
• Abstract summary: The presented approach demonstrates an automated system for breast tumor segmentation from 18F-FDG PET. Thanks to the extracted biomarkers, our method enables the automatic assessment of cancer progression.
• Score: 34.998498202537185
• License:
• Abstract: Neoadjuvant chemotherapy (NAC) has become a standard clinical practice for tumor downsizing in breast cancer with 18F-FDG Positron Emission Tomography (PET). Our work aims to leverage PET imaging for the segmentation of breast lesions. The focus is on developing an automated system that accurately segments primary tumor regions and extracts key biomarkers from these areas to provide insights into the evolution of breast cancer following the first course of NAC. 243 baseline 18F-FDG PET scans (PET_Bl) and 180 follow-up 18F-FDG PET scans (PET_Fu) were acquired before and after the first course of NAC, respectively. Firstly, a deep learning-based breast tumor segmentation method was developed. The optimal baseline model (model trained on baseline exams) was fine-tuned on 15 follow-up exams and adapted using active learning to segment tumor areas in PET_Fu. The pipeline computes biomarkers such as maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) to evaluate tumor evolution between PET_Fu and PET_Bl. Quality control measures were employed to exclude aberrant outliers. The nnUNet deep learning model outperformed in tumor segmentation on PET_Bl, achieved a Dice similarity coefficient (DSC) of 0.89 and a Hausdorff distance (HD) of 3.52 mm. After fine-tuning, the model demonstrated a DSC of 0.78 and a HD of 4.95 mm on PET_Fu exams. Biomarkers analysis revealed very strong correlations whatever the biomarker between manually segmented and automatically predicted regions. The significant average decrease of SUVmax, MTV and TLG were 5.22, 11.79 cm3 and 19.23 cm3, respectively. The presented approach demonstrates an automated system for breast tumor segmentation from 18F-FDG PET. Thanks to the extracted biomarkers, our method enables the automatic assessment of cancer progression.

Related papers

• 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)
• 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)
• AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net [30.142259166452693]
  FDG-PET scans may misinterpret irregular glucose consumption in healthy tissues as cancer. The AutoPET challenge addresses this by providing a dataset of 1014 FDG-PET/CT studies.
  arXiv  Detail & Related papers  (2023-09-21T14:34:17Z)
• Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction [62.29541106695824]
  This paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM) By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process.
  arXiv  Detail & Related papers  (2023-08-20T04:10:36Z)
• 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)
• Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks [2.735686397209314]
  The goal of this study was to report the performance of a deep neural network designed to automatically segment regions suspected of cancer in whole-body 18F-FDG PET/CT images. A cascaded approach was developed where a stacked ensemble of 3D UNET CNN processed the PET/CT images at a fixed 6mm resolution.
  arXiv  Detail & Related papers  (2022-10-14T19:25:56Z)
• PriorNet: lesion segmentation in PET-CT including prior tumor appearance information [0.0]
  We propose a two-step approach to improve the segmentation performances of tumoral lesions in PET-CT images. The first step generates a prior tumor appearance map from the PET-CT volumes, regarded as prior tumor information. The second step, consisting of a standard U-Net, receives the prior tumor appearance map and PET-CT images to generate the lesion mask.
  arXiv  Detail & Related papers  (2022-10-05T12:31:42Z)
• 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)
• Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation [11.622615048002567]
  Multimodal spatial attention module (MSAM) learns to emphasize regions related to tumors. MSAM can be applied to common backbone architectures and trained end-to-end.
  arXiv  Detail & Related papers  (2020-07-29T10:27:22Z)
• 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.