Radiomics and deep learning methods for the prediction of 2-year overall survival in LUNG1 dataset

Braghetto, Anna; Marturano, Francesca; Paiusco, M.; Baiesi, Marco; Bettinelli, Andrea

doi:10.1038/s41598-022-18085-z

Cited by 16 publications

(10 citation statements)

References 29 publications

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“…However, they remain restricted in their applicability in such low-data scenarios due to their dependency on large amounts of data to provide robust performance. Training deep-learning models on small cohorts often lead to overfitting, which diminishes performance when external data is introduced 11 . Our foundation model approach has several innovations: first, we developed a deep-learning system on a large corpus of 3D lesion images with considerable diversity in their presentation.…”

Section: Discussionmentioning

confidence: 99%

“…While many studies investigating imaging-based biomarkers incorporate supervised deep learning algorithms into their models [9][10][11] , they are typically applied in scenarios where large datasets are available for training and testing. The quantity and quality of annotated data are strongly linked to the robustness of deep learning models.…”

Section: Introductionmentioning

confidence: 99%

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Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging

Pai,

Bontempi,

Hadzic

et al. 2023

Preprint

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Foundation models represent a recent paradigm shift in deep learning, where a single large-scale model trained on vast amounts of data can serve as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labeled datasets are often scarce. Here, we developed a foundation model for imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of imaging-based biomarkers. We found that they facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed their conventional supervised counterparts on downstream tasks. The performance gain was most prominent when training dataset sizes were very limited. Furthermore, foundation models were more stable to input and inter-reader variations and showed stronger associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering novel imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging

Pai,

Bontempi,

Hadzic

et al. 2023

Preprint

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show abstract

“…By combining 2D and 3D features, Liu et al (2022) increased the accuracy rate from 77% to 82%. In addition, the methods that utilize DL and radiomics features simultaneously have been shown to perform satisfactorily on the medical image classification task (Braghetto et al 2022, Beuque et al 2023. Yang et al (2022) reduced the false positive rate through the application of radiomics in DL based segmentation tasks.…”

Section: And Radiomics Methodsmentioning

confidence: 99%

Sacroiliitis diagnosis based on interpretable features and multi-task learning

Liu,

Zhang,

Zhang

et al. 2024

Phys. Med. Biol.

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Objective. Sacroiliitis is an early pathological manifestation of ankylosing spondylitis (AS), and a positive sacroiliitis test on imaging may help clinical practitioners diagnose AS early. Deep learning based automatic diagnosis algorithms can deliver grading findings for sacroiliitis, however, it requires a large amount of data with precise labels to train the model and lacks grading features visualization. In this paper, we aimed to propose a radiomics and deep learning based deep feature visualization positive diagnosis algorithm for sacroiliitis on CT scans. Visualization of grading features can enhance clinical interpretability with visual grading features, which assist doctors in diagnosis and treatment more effectively.Approach. The region of interest (ROI) is identified by segmenting the sacroiliac joint (SIJ) 3D CT images using a combination of the U-net model and certain statistical approaches. Then, in addition to extracting spatial and frequency domain features from ROI according to the radiographic manifestations of sacroiliitis, the radiomics features have also been integrated into the proposed encoder module to obtain a powerful encoder and extract features effectively. Finally, a multi-task learning technique and five-class labels are utilized to help with performing positive tests to reduce discrepancies in the evaluation of several radiologists.Main results. On our private dataset, proposed methods have obtained an accuracy rate of 87.3$\%$, which is 9.8$\%$ higher than the baseline and consistent with assessments made by qualified medical professionals.Significance. The results of the ablation experiment and interpreting analysis demonstrated that the proposed methods are applied in automatic CT scan sacroiliitis diagnosis due to their excellently interpretable and portable advantages.

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“…One of the drawbacks of this work is that their model was validated on a small dataset consisting of 21 patients, and larger cohorts were needed to confirm the results to translate them to a clinical setting. Braghetto et al compared imaging signatures derived from engineered features and deep learning-based features to predict the 2-year OS in NSCLC patients leveraging the publicly available lung cancer dataset [ 14 ]. A recent comparative study by Li et al the authors used 3 machine learning models along with EHR data to build predictive models for clinical endpoints using data from a single institution [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics

Yolchuyeva,

Ebrahimpour,

Tonneau

et al. 2024

J Transl Med

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Background Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy. Methods Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance. Results From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts). Conclusion The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.

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Radiomics and deep learning methods for the prediction of 2-year overall survival in LUNG1 dataset

Cited by 16 publications

References 29 publications

Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging

Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging

Sacroiliitis diagnosis based on interpretable features and multi-task learning

Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics

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