Deep Learning-Based CT Imaging in Diagnosing Myeloma and Its Prognosis Evaluation

Wang, Jinzhou; Shi, Xiangjun; Yao, Xingchen; Ren, Jie; Du, Xinru

doi:10.1155/2021/5436793

Cited by 5 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These alternative algorithms achieved DSCs or AUCs above 0.7, which is on par with the median performance of the U-Net models. However, U-Net variations have been tried in a greater number of studies and demonstrated performance as high as 0.9821 in this cohort ( 58 ), indicating that U-Net may be more suitable at present day for achieving maximal performance.…”

Section: Discussionmentioning

confidence: 83%

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

Rich,

Bhardwaj,

Shah

et al. 2023

Front. Radiol.

View full text Add to dashboard Cite

IntroductionImage segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT).MethodThe literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review.ResultsThe majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85–0.9.DiscussionDeep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.

show abstract

Section: Discussionmentioning

confidence: 83%

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

Rich,

Bhardwaj,

Shah

et al. 2023

Front. Radiol.

View full text Add to dashboard Cite

show abstract

“…Of the 33 studies that applied ML techniques to diagnose a hematological malignancy or to differentiate it from another disease state or malignancy ( Table 1 ), 18 were designed to establish and train ML models to discriminate gliomas [predominantly GBM from PCNSL ( 29 – 31 , 34 – 37 , 40 – 42 , 44 , 45 , 49 , 50 , 53 – 56 )] using features extracted from FDG-PET [one study ( 29 ),] or MRI ( 30 , 31 , 34 – 37 , 40 – 42 , 44 , 45 , 49 , 50 , 53 – 56 ) images. The remaining studies belonged to two major categories: those developing models to discriminate solid hematological malignancies from other benign and malignant lesions at other sites [nasopharyngeal carcinomas from nasopharyngeal lymphoma ( 46 , 48 ), idiopathic orbital inflammation from ocular adnexal lymphoma ( 33 ), thymic neoplasm from thymic lymphoma ( 14 ), breast carcinoma from breast lymphoma ( 15 ), lymphoma from normal nodes ( 43 ), or multiple myeloma from bone metastases ( 51 )] and those that detect the location of hematological malignancies either at diagnosis or during the disease course [location of ( 18 ) or evolving/residual lymphoma ( 32 ) or leukemia ( 17 ) or bone marrow involvement with multiple myeloma ( 16 , 38 , 47 , 52 ) or mantle cell lymphoma ( 39 )].…”

Section: Resultsmentioning

confidence: 99%

The application of machine learning to imaging in hematological oncology: A scoping review

Kotsyfakis¹,

Iliaki-Giannakoudaki²,

Anagnostopoulos³

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

BackgroundHere, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps.MethodsThe review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle–Ottawa scale was used to assess the quality of observational studies.ResultsOf 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case–control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation.ConclusionTo deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.

show abstract

“…Therefore, it is necessary to improve the quality of CT images. In recent years, the use of artificial intelligence algorithms for processing medical images is a major trend in medical imaging [ 20 ]. Threshold segmentation on the basis of intelligent algorithms is the most common method for image segmentation.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Heart Failure Complicated with Sleep Apnea Syndrome by Thoracic Computerized Tomography under Artificial Intelligence Algorithm

Tian

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The aim of this study was to explore the application effect of thoracic computerized tomography (CT) under single threshold segmentation algorithm in the diagnosis of heart failure (HF) complicated with sleep apnea syndrome. 30 patients diagnosed with HF complicated with sleep apnea syndrome were chosen for the research. Another 30 patients without sleep apnea syndrome were selected as the control group, whose age, height, and weight were similar to those of the experimental group. Then, a model for thoracic CT image segmentation was proposed under the single threshold segmentation algorithm, and the faster region convolutional neural network (Faster RCNN) was applied to label the thoracic respiratory lesions. All the patients underwent thoracic CT examination, and the obtained images were processed using the algorithm model above. After that, the morphology of the patient’s respiratory tract after treatment was observed. The results suggested that the improved single threshold segmentation algorithm was effective for the image segmentation of patient lesions, and the Faster RCNN could effectively finish the labeling of the lesion area in the CT image. The classification accuracy of the Faster RCNN was about 0.966, and the loss value was about 0.092. With CT scanning under the algorithm, it was found that the airway collapse of the posterior palatal area, retrolingual area, and laryngopharyngeal area of the sleep apnea syndrome patients was significantly greater than that of the control group ( P < 0.05 ). But there was no significant difference of the collapse of the nasopharyngeal area between the two groups ( P > 0.05 ). The single threshold segmentation algorithm had a better segmentation accuracy for thoracic CT images in patients with HF and sleep apnea syndrome, so it had a highly promising application prospect in the diagnosis of the disease.

show abstract

Deep Learning-Based CT Imaging in Diagnosing Myeloma and Its Prognosis Evaluation

Cited by 5 publications

References 29 publications

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

The application of machine learning to imaging in hematological oncology: A scoping review

Diagnosis of Heart Failure Complicated with Sleep Apnea Syndrome by Thoracic Computerized Tomography under Artificial Intelligence Algorithm

Contact Info

Product

Resources

About