Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)

Lisson, Catharina Silvia; Lisson, Christoph; Achilles, Sherin; Mezger, M.; Wolf, Daniel; Schmidt, Stefan A.; Thaiss, Wolfgang M.; Bloehdorn, Johannes; Beer, Ambros; Stilgenbauer, Stephan; Beer, Meinrad; Goetz, Michael

doi:10.3390/cancers14020393

Cited by 4 publications

(1 citation statement)

References 59 publications

(94 reference statements)

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“…While there are some studies that make use of automatic segmentations [48,49], this is not always true. We assume that the comparatively small size of many studies [16,50,51] hinders the researcher in investing additional efforts. However, given the original capabilities of the baseline, we expect that our solution can provide an easy approach to develop automatic segmentation solutions even with limited data.…”

Section: Discussionmentioning

confidence: 99%

Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

et al. 2022

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A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.

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Section: Discussionmentioning

confidence: 99%

Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

et al. 2022

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Association of Metabolomics with AI in Precision Oncology: Emerging Perspectives for More Effective Cancer Care

Mbodi¹,

Mathebela²,

Dlamini³

2023

Artificial Intelligence and Precision Oncology

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Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT

et al. 2023

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Purpose The radiologists’ workload is increasing, and computational imaging techniques may have the potential to identify visually unequivocal lesions, so that the radiologist can focus on equivocal and critical cases. The purpose of this study was to assess radiomics versus dual-energy CT (DECT) material decomposition to objectively distinguish visually unequivocal abdominal lymphoma and benign lymph nodes. Methods Retrospectively, 72 patients [m, 47; age, 63.5 (27–87) years] with nodal lymphoma (n = 27) or benign abdominal lymph nodes (n = 45) who had contrast-enhanced abdominal DECT between 06/2015 and 07/2019 were included. Three lymph nodes per patient were manually segmented to extract radiomics features and DECT material decomposition values. We used intra-class correlation analysis, Pearson correlation and LASSO to stratify a robust and non-redundant feature subset. Independent train and test data were applied on a pool of four machine learning models. Performance and permutation-based feature importance was assessed to increase the interpretability and allow for comparison of the models. Top performing models were compared by the DeLong test. Results About 38% (19/50) and 36% (8/22) of the train and test set patients had abdominal lymphoma. Clearer entity clusters were seen in t-SNE plots using a combination of DECT and radiomics features compared to DECT features only. Top model performances of AUC = 0.763 (CI = 0.435–0.923) were achieved for the DECT cohort and AUC = 1.000 (CI = 1.000–1.000) for the radiomics feature cohort to stratify visually unequivocal lymphomatous lymph nodes. The performance of the radiomics model was significantly (p = 0.011, DeLong) superior to the DECT model. Conclusions Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment.

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Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)

Cited by 4 publications

References 59 publications

Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

Association of Metabolomics with AI in Precision Oncology: Emerging Perspectives for More Effective Cancer Care

Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT

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