Potential of artificial intelligence based on chest computed tomography to predict the nature of part‐solid nodules
Abstract Background The potential of artificial intelligence (AI) to predict the nature of part‐solid nodules based on chest computed tomography (CT) is still under exploration. Objective To determine the potential of AI to predict the nature of part‐solid nodules. Methods Two hundred twenty‐three p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
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| Series: | The Clinical Respiratory Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/crj.13597 |
| Summary: | Abstract Background The potential of artificial intelligence (AI) to predict the nature of part‐solid nodules based on chest computed tomography (CT) is still under exploration. Objective To determine the potential of AI to predict the nature of part‐solid nodules. Methods Two hundred twenty‐three patients diagnosed with part‐solid nodules (241) by chest CT were retrospectively collected that were divided into benign group (104) and malignant group (137). Intraclass correlation coefficient (ICC) was used to assess the agreement in predicting malignancy, and the predictive effectiveness was compared between AI and senior radiologists. The parameters measured by AI and the size of solid components measured by senior radiologists were compared between two groups. Receiver operating characteristic (ROC) curve was chosen for calculating the Youden index of each quantitative parameter, which has statistical significance between two groups. Binary logistic regression performed on the significant indicators to suggest predictors of malignancy. Results AI was in moderate agreement with senior radiologists (ICC = 0.686). The sensitivity, specificity and accuracy of two groups were close (p > 0.05). The longest diameter, volume and mean CT attenuation value and the largest diameter of solid components between benign and malignant groups were different significantly (p < 0.001). Logistic regression analysis showed that the longest diameter and mean CT attenuation value and the largest diameter of solid components were indicators for malignant part‐solid nodules, the threshold of which were 9.45 mm, 425.0 HU and 3.45 mm, respectively. Conclusion Potential of quantitative parameter measured by AI to predict malignant part‐solid nodules can provide a certain value for the clinical management. |
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| ISSN: | 1752-6981 1752-699X |