โก Quick Summary
This study presents a novel approach for the label-free histological identification of intraductal carcinoma of the prostate (IDC-P) using texture analysis-based machine learning combined with multimodal nonlinear optical imaging. The results indicate a remarkable mean classification accuracy of 98% when using combined imaging techniques, providing a potential breakthrough in prostate cancer diagnostics.
๐ Key Details
- ๐ Imaging Techniques: Second-harmonic generation (SHG) and stimulated Raman scattering (SRS) at 1450 cm-1 and 1668 cm-1
- ๐งฉ Analysis Method: Texture analysis-based statistics derived from gray-level co-occurrence matrix
- โ๏ธ Machine Learning Model: Support vector machine (SVM)
- ๐ Performance: Mean classification accuracy of 89% for SHG or SRS images, and 98% for combined images
๐ Key Takeaways
- ๐ฌ IDC-P is a highly aggressive subtype of prostate cancer with poor clinical outcomes.
- ๐ก Novel Methodology combines texture analysis with advanced imaging techniques for accurate diagnosis.
- ๐ High Accuracy achieved with SVM models, indicating strong potential for clinical application.
- ๐ Multimodal Imaging enhances the ability to distinguish between IDC-P, high-grade PCa, low-grade PCa, and benign tissue.
- ๐จโโ๏ธ Pathologists could benefit from this reliable biomarker for improved diagnostic accuracy.
- ๐ Study Published in Scientific Reports, highlighting its significance in the field of oncology.
๐ Background
Prostate cancer remains one of the most prevalent cancers among men, with intraductal carcinoma of the prostate (IDC-P) representing a particularly aggressive form. Unfortunately, the lack of accurate biomarkers for IDC-P complicates diagnosis and treatment, often leading to poor clinical outcomes. The integration of advanced imaging techniques and machine learning offers a promising avenue for enhancing diagnostic precision in this challenging area of oncology.
๐๏ธ Study
The study conducted by Gagnon et al. utilized multimodal nonlinear optical imaging techniques, specifically second-harmonic generation (SHG) and stimulated Raman scattering (SRS), to analyze prostate tissue samples. By extracting texture-based statistics from the images, the researchers developed a machine learning model using support vector machines (SVM) to classify different types of prostate tissue, including IDC-P, high-grade PCa, low-grade PCa, and benign tissue.
๐ Results
The findings revealed that the SVM models trained on either SHG or SRS images achieved a mean classification accuracy exceeding 89%. Remarkably, when combining both imaging modalities, the accuracy soared to an impressive 98%. These results underscore the effectiveness of the proposed methodology in accurately distinguishing IDC-P from other prostate tissue types.
๐ Impact and Implications
The implications of this study are profound. By providing a reliable biomarker for IDC-P, this research could significantly enhance the diagnostic capabilities of pathologists, leading to better patient management and treatment outcomes. The integration of multimodal imaging and machine learning not only represents a technological advancement but also paves the way for future innovations in cancer diagnostics, potentially transforming how we approach prostate cancer.
๐ฎ Conclusion
This study highlights the transformative potential of combining multimodal nonlinear optical imaging with texture analysis-based machine learning in the identification of intraductal carcinoma of the prostate. With a mean classification accuracy of 98%, this approach offers a promising new tool for pathologists, enhancing diagnostic accuracy and ultimately improving patient care. Continued research in this area is essential to further validate and refine these techniques for clinical use.
๐ฌ Your comments
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Label-free histological identification of intraductal carcinoma of the prostate using texture analysis-based multimodal stimulated Raman scattering microscopy.
Abstract
Intraductal carcinoma of the prostate (IDC-P) is a very aggressive histopathological subtype of prostate cancer (PCa) that is strongly associated with poor clinical outcomes but for which no accurate biomarkers exist. Here, we demonstrate a novel application of texture analysis-based machine learning alongside multimodal nonlinear optical imaging using second-harmonic generation (SHG) and stimulated Raman scattering (SRS) at 1450ย cm-1 and 1668ย cm-1 Raman shifts to distinguish IDC-P from regular PCa and benign prostate. Images from each tissue type were analyzed to extract the first-order statistics and texture-based second-order statistics derived from the gray-level co-occurrence matrix of the images. A machine learning model was constructed using support vector machine (SVM) to classify the prostate tissue based on these statistics. Our results demonstrate that SVM models trained on either SHG or SRS images accurately classify IDC-P as well as high-grade PCa, low-grade PCa, and benign tissue with a mean classification accuracy exceeding 89%. Moreover, a mean classification accuracy of 98% was achieved using an SVM model trained on combined SHG and SRS images. Our study demonstrates that multimodal nonlinear optical imaging using SHG and SRS can be combined with texture analysis-based SVM classification to provide pathologists with a reliable biomarker of IDC-P.
Author: [‘Gagnon JR’, ‘Allen CH’, ‘Diop MK’, ‘Dallaire F’, ‘Leblond F’, ‘Trudel D’, ‘Murugkar S’]
Journal: Sci Rep
Citation: Gagnon JR, et al. Label-free histological identification of intraductal carcinoma of the prostate using texture analysis-based multimodal stimulated Raman scattering microscopy. Label-free histological identification of intraductal carcinoma of the prostate using texture analysis-based multimodal stimulated Raman scattering microscopy. 2025; 15:39874. doi: 10.1038/s41598-025-23780-8