Cross-Vendor Reproducibility of Radiomics-based Machine Learning Models for Computer-aided Diagnosis
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929436999286784 |
|---|---|
| author | Chaudhary, Jatin Jambor, Ivan Aronen, Hannu Ettala, Otto Saunavaara, Jani Boström, Peter Heikkonen, Jukka Kanth, Rajeev Merisaari, Harri |
| author_facet | Chaudhary, Jatin Jambor, Ivan Aronen, Hannu Ettala, Otto Saunavaara, Jani Boström, Peter Heikkonen, Jukka Kanth, Rajeev Merisaari, Harri |
| contents | Background: The reproducibility of machine-learning models in prostate cancer detection across different MRI vendors remains a significant challenge. Methods: This study investigates Support Vector Machines (SVM) and Random Forest (RF) models trained on radiomic features extracted from T2-weighted MRI images using Pyradiomics and MRCradiomics libraries. Feature selection was performed using the maximum relevance minimum redundancy (MRMR) technique. We aimed to enhance clinical decision support through multimodal learning and feature fusion. Results: Our SVM model, utilizing combined features from Pyradiomics and MRCradiomics, achieved an AUC of 0.74 on the Multi-Improd dataset (Siemens scanner) but decreased to 0.60 on the Philips test set. The RF model showed similar trends, with notable robustness for models using Pyradiomics features alone (AUC of 0.78 on Philips). Conclusions: These findings demonstrate the potential of multimodal feature integration to improve the robustness and generalizability of machine-learning models for clinical decision support in prostate cancer detection. This study marks a significant step towards developing reliable AI-driven diagnostic tools that maintain efficacy across various imaging platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18060 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-Vendor Reproducibility of Radiomics-based Machine Learning Models for Computer-aided Diagnosis Chaudhary, Jatin Jambor, Ivan Aronen, Hannu Ettala, Otto Saunavaara, Jani Boström, Peter Heikkonen, Jukka Kanth, Rajeev Merisaari, Harri Machine Learning Background: The reproducibility of machine-learning models in prostate cancer detection across different MRI vendors remains a significant challenge. Methods: This study investigates Support Vector Machines (SVM) and Random Forest (RF) models trained on radiomic features extracted from T2-weighted MRI images using Pyradiomics and MRCradiomics libraries. Feature selection was performed using the maximum relevance minimum redundancy (MRMR) technique. We aimed to enhance clinical decision support through multimodal learning and feature fusion. Results: Our SVM model, utilizing combined features from Pyradiomics and MRCradiomics, achieved an AUC of 0.74 on the Multi-Improd dataset (Siemens scanner) but decreased to 0.60 on the Philips test set. The RF model showed similar trends, with notable robustness for models using Pyradiomics features alone (AUC of 0.78 on Philips). Conclusions: These findings demonstrate the potential of multimodal feature integration to improve the robustness and generalizability of machine-learning models for clinical decision support in prostate cancer detection. This study marks a significant step towards developing reliable AI-driven diagnostic tools that maintain efficacy across various imaging platforms. |
| title | Cross-Vendor Reproducibility of Radiomics-based Machine Learning Models for Computer-aided Diagnosis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.18060 |