Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

Yang, Jiancheng; Fang, Rongyao; Ni, Bingbing; Li, Yamin; Xu, Yi; Li, Linguo

doi:10.1007/978-3-030-32226-7_73

Cited by 20 publications

(11 citation statements)

References 11 publications

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“…Radiomic flow is a complex process, and every aspect of the image acquisition, such as defining and contouring the regions of interests, and choosing the best features to be extracted and the proper statistics to be applied, remains challenging. Lately, explainable AI (XAI), using DNNs (deep neural networks), may help radiomics in classification and prediction in the clinical setting [ 174 , 175 ], and a controllable and explainable probabilistic radiomics framework was proposed, through which a 3D CNN feature is extracted upon the lesion region only and is used to approximate the ambiguity distribution over human experts [ 176 ]. These new features will have to be further validated.…”

Section: Discussionmentioning

confidence: 99%

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Ferro

Cobelli

Vartolomei

et al. 2021

IJMS

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Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

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Section: Discussionmentioning

confidence: 99%

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Ferro

Cobelli

Vartolomei

et al. 2021

IJMS

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“…Numerous computer-assisted tools have been introduced based on image processing, conventional machine learning, as well as advanced CNN models. CNN-based features are indeed abstract, and there is no guarantee that the black-box models with limited supervision derive the classification results by looking at the nodule lesions [38,52]. Moreover, previous studies have shown that separating image features extracted from inside the nodule and from the region immediately outside the nodule could lead to better classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features

et al. 2021

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Low-Dose Computed Tomography (LDCT) is the most common imaging modality for lung cancer diagnosis. The presence of nodules in the scans does not necessarily portend lung cancer, as there is an intricate relationship between nodule characteristics and lung cancer. Therefore, benign-malignant pulmonary nodule classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features. Methods: To efficiently capture both intra-nodule heterogeneities and contextual information of the pulmonary nodules, a dual pathway model was developed to integrate the intra-nodule characteristics with contextual attributes. The proposed approach was implemented with both supervised and unsupervised learning schemes. A random forest model was added as a second component on top of the networks to generate the classification results. The discrimination power of the model was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUROC) metric. Results: Experiments on 1297 manually segmented nodules show that the integration of context and target supervised deep features have a great potential for accurate prediction, resulting in a discrimination power of 0.936 in terms of AUROC, which outperformed the classification performance of the Kaggle 2017 challenge winner. Conclusion:Empirical results demonstrate that integrating nodule target and context images into a unified network improves the discrimination power, outperforming the conventional single pathway convolutional neural networks.

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“…Following studies introduces sophisticated feature aggregation based on spatial graphs [13,10] or attention [24]. However, only a few studies have applied deep shape analysis in medical imaging scenarios [25,20,22].…”

Section: Rib Segmentation From a Viewpoint Of Point Cloudsmentioning

confidence: 99%

RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans

Yang,

Gu,

Wei

et al. 2021

Preprint

Self Cite

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Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes. Automatic rib segmentation methods can speed up the process through rib measurement and visualization. However, prior arts mostly use in-house labeled datasets that are publicly unavailable and work on dense 3D volumes that are computationally inefficient. To address these issues, we develop a labeled rib segmentation benchmark, named RibSeg, including 490 CT scans (11,719 individual ribs) from a public dataset. For ground truth generation, we used existing morphology-based algorithms and manually refined its results. Then, considering the sparsity of ribs in 3D volumes, we thresholded and sampled sparse voxels from the input and designed a point cloud-based baseline method for rib segmentation. The proposed method achieves stateof-the-art segmentation performance (Dice ≈ 95%) with significant efficiency (10 ∼ 40× faster than prior arts). The RibSeg dataset, code, and model in PyTorch are available at https://github.com/M3DV/RibSeg.

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Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

Cited by 20 publications

References 11 publications

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features

RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans

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