Single Shot AI-assisted quantification of KI-67 proliferation index in breast cancer

• URL: http://arxiv.org/abs/2503.19606v1
• Date: Tue, 25 Mar 2025 12:41:45 GMT
• Title: Single Shot AI-assisted quantification of KI-67 proliferation index in breast cancer
• Authors: Deepti Madurai Muthu, Priyanka S, Lalitha Rani N, P. G. Kubendran Amos,
• Abstract summary: This study introduces an AI-assisted method using the YOLOv8 object detection framework for automated Ki-67 scoring.<n>High-resolution digital images (40x magnification) ofchemically stained tumor sections were captured from Ki-67 hotspot regions and manually annotated by a domain expert to distinguish Ki-67-positive and negative tumor cells.<n>The proposed approach offers an efficient, scalable, and objective alternative to conventional scoring methods, supporting greater consistency in Ki-67 evaluation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable quantification of Ki-67, a key proliferation marker in breast cancer, is essential for molecular subtyping and informed treatment planning. Conventional approaches, including visual estimation and manual counting, suffer from interobserver variability and limited reproducibility. This study introduces an AI-assisted method using the YOLOv8 object detection framework for automated Ki-67 scoring. High-resolution digital images (40x magnification) of immunohistochemically stained tumor sections were captured from Ki-67 hotspot regions and manually annotated by a domain expert to distinguish Ki-67-positive and negative tumor cells. The dataset was augmented and divided into training (80%), validation (10%), and testing (10%) subsets. Among the YOLOv8 variants tested, the Medium model achieved the highest performance, with a mean Average Precision at 50% Intersection over Union (mAP50) exceeding 85% for Ki-67-positive cells. The proposed approach offers an efficient, scalable, and objective alternative to conventional scoring methods, supporting greater consistency in Ki-67 evaluation. Future directions include developing user-friendly clinical interfaces and expanding to multi-institutional datasets to enhance generalizability and facilitate broader adoption in diagnostic practice.

Related papers

• A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers [51.45596445363302]
  GlobeReady is a clinician-friendly AI platform that enables ocular disease diagnosis without retraining/fine-tuning or technical expertise. It achieves high accuracy across imaging modalities: 93.9-98.5% for an 11-category fundus photo dataset and 87.2-92.7% for a 15-category OCT dataset. It addresses domain shifts across centers and populations, reaching an average accuracy of 88.9% across five centers in China, 86.3% in Vietnam, and 90.2% in the UK.
  arXiv  Detail & Related papers  (2025-04-22T14:17:22Z)
• Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer [0.4660328753262075]
  This study leverages machine learning and deep learning approaches, along with feature selection and extraction methods, to enhance PCa pathological staging predictions.<n>Gene expression profiles from 486 tumors were analyzed using advanced algorithms, including Random Forest (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM)<n>The results reveal that the highest test F1-score, approximately 83%, was achieved by the Random Forest algorithm.
  arXiv  Detail & Related papers  (2025-02-13T14:53:09Z)
• ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease Assessment [42.72554952799386]
  We present ADformer, a novel multi-granularity transformer designed to capture temporal and spatial features to learn effective EEG representations. We conduct experiments across 5 datasets with a total of 525 subjects in setups including subject-dependent, subject-independent, and leave-subjects-out. Our results show that ADformer outperforms existing methods in most evaluations.
  arXiv  Detail & Related papers  (2024-08-17T14:10:41Z)
• Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge [44.76736949127792]
  We describe the design and results from the BraTS 2023 Intracranial Meningioma Challenge.<n>The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas.<n>The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor.
  arXiv  Detail & Related papers  (2024-05-16T03:23:57Z)
• Neural Network-Based Histologic Remission Prediction In Ulcerative Colitis [38.150634108667774]
  Histologic remission is a new therapeutic target in ulcerative colitis (UC) Endocytoscopy (EC) is a novel ultra-high magnification endoscopic technique. We propose a neural network model that can assess histological disease activity in EC images.
  arXiv  Detail & Related papers  (2023-08-28T15:54:14Z)
• PathRTM: Real-time prediction of KI-67 and tumor-infiltrated lymphocytes [0.0]
  We introduce PathRTM, a novel deep neural network detector based on RTMDet, for automated KI-67 proliferation and tumor-infiltrated lymphocyte estimation. PathRTM achieves state-of-the-art performance in KI-67 immunopositive, immunonegative, and lymphocyte detection, with an average precision (AP) of 41.3%.
  arXiv  Detail & Related papers  (2023-04-23T08:17:26Z)
• Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps [48.498376125522114]
  This paper proposes a UNet-based segmentation approach that requires minimal post-processing. It achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy.
  arXiv  Detail & Related papers  (2022-10-13T15:34:20Z)
• Ki-67 Index Measurement in Breast Cancer Using Digital Image Analysis [0.0]
  The Ki67 index is a valuable prognostic variable in several kinds of cancer. In clinical practice, the measurement of Ki-67 index relies on visual identifying method and manual counting. Here, we use digital image processing technics to create a digital image analysis method to interpretate Ki-67 index.
  arXiv  Detail & Related papers  (2022-09-27T04:48:57Z)
• 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)
• Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism [65.70943212672023]
  Deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs. dataset of 70 panoramic radiographs (PRs) from 70 different subjects of age between 49 to 60 was used.
  arXiv  Detail & Related papers  (2021-10-19T00:03:57Z)
• Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation [44.02622933605018]
  Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM) We aimed to compare nine machine learning classifiers to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,
  arXiv  Detail & Related papers  (2021-02-10T15:10:37Z)
• PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline [2.4378845585726903]
  We introduce a novel pipeline and a backend, namely PathoNet for Ki-67 immunostained cell detection and classification. Despite facing challenges, our proposed backend, PathoNet, outperforms the state of the art methods proposed to date in the harmonic mean measure.
  arXiv  Detail & Related papers  (2020-10-09T17:55:52Z)

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.