Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

Guo, Rui; Hu, Xiaobin; Song, Haoming; Xu, Pengpeng; Xu, Haoping; Rominger, Axel; Lin, Xiaozhu; Menze, Bjoern; Li, Biao; Shi, Kuangyu

doi:10.1007/s00259-021-05232-3

Cited by 28 publications

(19 citation statements)

References 51 publications

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“…Nine studies applied ML to prognostication or predicting responses to therapy in patients with hematological malignancies ( Table 3 ): (i) predicting outcomes (overall survival or progression-free survival) in patients with extranodal NK/T-cell lymphoma, nasal type ( 69 ), multiple myeloma ( 70 , 75 ), DLBCL ( 71 ), and mantle cell lymphoma ( 73 ) or (ii) predicting responses to therapy in patients with DLBCL ( 68 , 76 ) and HL ( 74 ). One study aimed to identify high-risk cytogenetic (HRC) multiple myeloma patients by applying ML to MRI images ( 72 ).…”

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.

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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.

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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.

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“…One approach to achieve this is to invite others researchers to participate in the development and we therefore make our CNN available to others. Another approach is to use methods to maximize the utility of incomplete and missing data as presented by Guo and co-workers [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer

et al. 2022

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Background Metabolic positron emission tomography/computed tomography (PET/CT) parameters describing tumour activity contain valuable prognostic information, but to perform the measurements manually leads to both intra- and inter-reader variability and is too time-consuming in clinical practice. The use of modern artificial intelligence-based methods offers new possibilities for automated and objective image analysis of PET/CT data. Purpose We aimed to train a convolutional neural network (CNN) to segment and quantify tumour burden in [18F]-fluorodeoxyglucose (FDG) PET/CT images and to evaluate the association between CNN-based measurements and overall survival (OS) in patients with lung cancer. A secondary aim was to make the method available to other researchers. Methods A total of 320 consecutive patients referred for FDG PET/CT due to suspected lung cancer were retrospectively selected for this study. Two nuclear medicine specialists manually segmented abnormal FDG uptake in all of the PET/CT studies. One-third of the patients were assigned to a test group. Survival data were collected for this group. The CNN was trained to segment lung tumours and thoracic lymph nodes. Total lesion glycolysis (TLG) was calculated from the CNN-based and manual segmentations. Associations between TLG and OS were investigated using a univariate Cox proportional hazards regression model. Results The test group comprised 106 patients (median age, 76 years (IQR 61–79); n = 59 female). Both CNN-based TLG (hazard ratio 1.64, 95% confidence interval 1.21–2.21; p = 0.001) and manual TLG (hazard ratio 1.54, 95% confidence interval 1.14–2.07; p = 0.004) estimations were significantly associated with OS. Conclusion Fully automated CNN-based TLG measurements of PET/CT data showed were significantly associated with OS in patients with lung cancer. This type of measurement may be of value for the management of future patients with lung cancer. The CNN is publicly available for research purposes.

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“…Physiologically based pharmacokinetic modelling may assist the translation of different radiopharmaceuticals during AI development [ 74 ]. And weakly supervised deep learning may accelerate the learning on incomplete datasets [ 75 ].…”

Section: Challenges For Transferability From Research To Clinical Pra...mentioning

confidence: 99%

Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation

Visvikis

Lambin

Mauridsen

et al. 2022

Eur J Nucl Med Mol Imaging

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Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as it will in everyday life. In this review, we focus on the potential applications of AI in the field, both from a physical (radiomics, underlying statistics, image reconstruction and data analysis) and a clinical (neurology, cardiology, oncology) perspective. Challenges for transferability from research to clinical practice are being discussed as is the concept of explainable AI. Finally, we focus on the fields where challenges should be set out to introduce AI in the field of nuclear medicine and molecular imaging in a reliable manner.

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Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

Cited by 28 publications

References 51 publications

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

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

Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer

Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation

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