Quantitative Imaging in Oncology

Sung, Wonmo

doi:10.3390/tomography8040139

Cited by 2 publications

(1 citation statement)

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“…The second task—recognition of the patterns related to lesions—relies on associated processes and requires significance experience from the reader, including the ability to engage in comparative interpretation [ 50 ]. Both steps are part of daily practice in radiology, with special importance in oncology, where lesion dimensions and associated change are of particular importance in treatment assessment and further planning [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Algorithm for Differentiating Patients with a Healthy Liver from Patients with Liver Lesions Based on MR Images

Skwirczyński

Tabor

Lasek

et al. 2023

Cancers

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The problems in diagnosing the state of a vital organ such as the liver are complex and remain unresolved. These problems are underscored by frequently published studies on this issue. At the same time, demand for imaging diagnostics, preferably using a method that can detect the disease at the earliest possible stage, is constantly increasing. In this paper, we present liver diseases in the context of diagnosis, diagnostic problems, and possible elimination. We discuss the dataset and methods and present the stages of the pipeline we developed, leading to multiclass segmentation of the liver in multiparametric MR image into lesions and normal tissue. Finally, based on the processing results, each case is classified as either a healthy liver or a liver with lesions. For the training set, the AUC ROC is 0.925 (standard error 0.013 and a p-value less than 0.001), and for the test set, the AUC ROC is 0.852 (standard error 0.039 and a p-value less than 0.001). Further refinements to the proposed pipeline are also discussed. The proposed approach could be used in the detection of focal lesions in the liver and the description of liver tumors. Practical application of the developed multi-class segmentation method represents a key step toward standardizing the medical evaluation of focal lesions in the liver.

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Section: Discussionmentioning

confidence: 99%