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Manual Delineation of the Region of Interest Combined With Clinical Image Analysis to Predict the Ki-67 Expression Level in Non-small Cell Lung Cancer.

Background: The Ki-67 antigen, a marker of cell proliferation, serves as a biomarker for assessing tumor malignancy. However, measuring Ki-67 levels through immunohistochemistry is often challenging due to difficulties in specimen collection and individual health issues. Radiological analysis has em...

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Published in:Sage Open Pathology Vol. 18; pp. 1 - 11
Main Authors: Li, Yizhi, Zhang, Jia, Lin, Xiaodan
Format: diagnostic images research tables/charts Journal Article
Published: Sage Publications Inc. 5/12/2025
Online Access:View this record in EBSCOhost
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      dt: 5/12/2025
      vid: 18
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: Manual Delineation of the Region of Interest Combined With Clinical Image Analysis to Predict the Ki-67 Expression Level in Non-small Cell Lung Cancer.
      aug:
        au:
          Li, Yizhi
          Zhang, Jia
          Lin, Xiaodan
        affil: Department of Radiation Therapy, Affillated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou Medical University, China
      sug:
        subj:
          Cell Proliferation
          Nuclear Proteins Metabolism
          Radiography, Thoracic Evaluation
          Carcinoma, Non-Small-Cell Lung Diagnosis
          Tumor Markers, Biological
          Antigens
          Human
          China
          Male
          Female
          Adult
          Middle Age
          Retrospective Design
          Record Review
          Logistic Regression
          Academic Medical Centers
          Support Vector Machine
          Confidence Intervals
          Mann-Whitney U Test
          Unpaired T-Tests
          Chi Square Test
          Data Analysis Software
          Disease Progression
          Immunohistochemistry
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Male
          Female
      ab: Background: The Ki-67 antigen, a marker of cell proliferation, serves as a biomarker for assessing tumor malignancy. However, measuring Ki-67 levels through immunohistochemistry is often challenging due to difficulties in specimen collection and individual health issues. Radiological analysis has emerged as a potential alternative for predicting Ki-67 levels, although its accuracy has been limited. This study aims to enhance the prediction of Ki-67 levels using chest X-rays by employing a refined approach that combines detailed, manually delineated radiological features with conventional imaging characteristics. Methods: This study collected X-ray images and Ki-67 expression data from 109 patients diagnosed with Non-Small Cell Lung Cancer (NSCLC). Seven radiological features related to tumor progression were annotated on each image by clinical professionals. Tumor areas were delineated using Python, resulting in the generation of 5 types of data from these regions. Data integration facilitated the development of predictive models utilizing Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN), with feature selection processes applied. Results: Using the RF, 8 predictive features were selected from the datasets, of which 7 exhibited a linear correlation with Ki-67 levels (Mantel-Haenszel test, P <.05). The model demonstrated robust performance metrics: Accuracy: 0.818, Precision: 0.823, Recall: 0.849, and F1 Score: 0.783. Conclusions: This research underscores the effectiveness of integrating specific radiological features, manually delineated regions of interest (ROIs), with traditional imaging characteristics and machine learning techniques. This approach significantly enhances the predictive accuracy of chest X-rays for Ki-67 levels, offering a non-invasive method for Ki-67 estimation.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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