Prognostic value of metabolic tumour volume and total lesion glycolysis measured by 18F‐fluorodeoxyglucose positron emission tomography/computed tomography in small cell lung cancer: A systematic review and meta‐analysis

Nie, Kai; Zhang, Yuxuan; Nie, Wei; Zhu, Lin; Chen, Yinan; Xiao, Yongxin; Liu, Shiyuan; Yu, Hong

doi:10.1111/1754-9485.12805

Cited by 10 publications

(4 citation statements)

References 58 publications

(102 reference statements)

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“…Just like any other type of cancer, SCLC cells use aerobic glycolysis as the main source of adenosine triphosphate [ 70 ]. Notably, higher total lesion glycolysis (TLG) is correlated with poorer prognosis of SCLC [ 71 ]. A-disintegrin-and-metalloproteinase 12-s (Adam12S), a protein related to tissue growth and development, promoted SCLC cell proliferation, migration, and invasion by upregulating the rate-limiting enzyme hexokinase 1 in glycolysis [ 72 ].…”

Section: Discussionmentioning

confidence: 99%

mRNA Network: Solution for Tracking Chemotherapy Insensitivity in Small-Cell Lung Cancer

Chen

et al. 2021

Journal of Healthcare Engineering

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Background. Small-cell lung cancer (SCLC) has poor prognosis and is prone to drug resistance. It is necessary to search for possible influencing factors for SCLC chemotherapy insensitivity. Therefore, we proposed an mRNA network to track the chemotherapy insensitivity in SCLC. Methods. Six samples of patients with SCLC were recruited for RNA sequencing. TopHat2 and Cufflinks were used to make differential analysis. Functional analysis was applied as well. Finally, multidimensional validation was applied for verifying the results we obtained by experiment. Results. This study was a trial of drug resistance in 6 SCLC patients after first-line chemotherapy. The top 10 downregulated genes differentially expressed in the chemo-insensitive group were SERPING1, DRD5, PARVG, PRAME, NKX1-1, MCTP2, PID1, PLEKHA4, SPP1, and SLN. Cell-cell signaling by Wnt ( p = 6.98 E − 21 ) was the most significantly enriched GO term in biological process, while systemic lupus erythematosus ( p = 6.97 E − 10 ), alcoholism ( p = 1.01 E − 09 ), and transcriptional misregulation in cancer ( p = 0.00227988 ) were the top three ones of KEGG pathways. In multiple public databases, we also highlighted and verified the vital role of glycolysis/gluconeogenesis pathway and corresponding genes in chemo-insensitivity in SCLC. Conclusion. Our study confirmed some SCLC chemotherapy insensitivity-related genes, biological processes, and pathways, thus constructing the chemotherapy-insensitive network for SCLC.

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Section: Discussionmentioning

confidence: 99%

mRNA Network: Solution for Tracking Chemotherapy Insensitivity in Small-Cell Lung Cancer

Chen

et al. 2021

Journal of Healthcare Engineering

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“…Overall, six meta-analyses containing information on the prognostic value of PET with different radiopharmaceuticals in patients with NETs and NECs were included (Table 2) [8,12,17,20,27,41]. These evidence-based manuscripts provided quantitative data about the prognostic value of 68 Ga-SSA PET or 18 F-FDG PET in patients with thoracic and/or gastroenteropancreatic NETs [8,17,20] or SCLC [12,27,41] in terms of disease control rate (DCR), event-free survival (EFS), progression-free survival (PFS) or overall survival (OS).…”

Section: Prognostic Value Of Pet With Different Radiopharmaceuticals In Neuroendocrine Neoplasmmentioning

confidence: 99%

“…Three different meta-analyses clearly demonstrated that 18 F-FDG PET has a significant prognostic value in patients with SCLC, in particular taking into account metabolic parameters at PET such as SUVmax of the primary lesion, metabolic tumor volume (MTV) or total lesion glycolysis (TLG) [12,27,41]. Higher values of these metabolic parameters were associated with a poorer prognosis regarding EFS, PFS and OS [12,27,41].…”

Section: Small Cell Lung Cancer (Sclc)mentioning

confidence: 99%

PET with Different Radiopharmaceuticals in Neuroendocrine Neoplasms: An Umbrella Review of Published Meta-Analyses

Treglia

Sadeghi

Giovinazzo

et al. 2021

Cancers

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Background: Several meta-analyses have reported quantitative data about the diagnostic performance, the prognostic value, the impact on management and the safety of positron emission tomography (PET) including related hybrid modalities (PET/CT or PET/MRI) using different radiopharmaceuticals in patients with neuroendocrine neoplasms. We performed an umbrella review of published meta-analyses to provide an evidence-based summary. Methods: A comprehensive literature search of meta-analyses listed in PubMed/MEDLINE and Cochrane Library databases was carried out (last search date: 30 June 2021). Results: Thirty-four published meta-analyses were selected and summarized. About the diagnostic performance: 68Ga-SSA PET yields high diagnostic performance in patients with NETs and PGL; 18F-FDOPA PET yields good diagnostic performance in patients with intestinal NETs, PGL, NB, being the best available PET method in detecting rMTC; 68Ga-exendin-4 PET has good diagnostic accuracy in detecting insulinomas; 18F-FDG PET has good diagnostic performance in detecting aggressive neuroendocrine neoplasms. About the prognostic value: 68Ga-SSA PET has a recognized prognostic value in well-differentiated NETs, whereas 18F-FDG PET has a recognized prognostic value in aggressive neuroendocrine neoplasms. A significant clinical impact of 68Ga-SSA PET and related hybrid modalities in patients with NETs was demonstrated. There are no major toxicities or safety issues related to the use of PET radiopharmaceuticals in patients with neuroendocrine neoplasms. Conclusions: Evidence-based data support the use of PET with different radiopharmaceuticals in patients with neuroendocrine neoplasms with specific indications for each radiopharmaceutical.

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“… 2 In addition to the maximum SUV, state-of-the-art metrics for assessing tumor burden also include the metabolic tumor volume (MTV) and total lesion glycolysis (TLG). 3 Although this information is, in principle, available in routine examinations, the evaluation can imply the manual analysis of a large number of single lesions and thus proves to be problematic in everyday clinical practice and in the exploration of large cohorts. Computer-aided automatic detection and segmentation of tumor lesions is therefore of great importance in PET/CT imaging.…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised segmentation of tumor lesions in PET-CT hybrid imaging

et al. 2021

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Purpose: We introduce and evaluate deep learning methods for weakly supervised segmentation of tumor lesions in whole-body fluorodeoxyglucose-positron emission tomography (FDG-PET) based solely on binary global labels ("tumor" versus "no tumor").Approach: We propose a three-step approach based on (i) a deep learning framework for image classification, (ii) subsequent generation of class activation maps (CAMs) using different CAM methods (CAM, GradCAM, GradCAM++, ScoreCAM), and (iii) final tumor segmentation based on the aforementioned CAMs. A VGG-based classification neural network was trained to distinguish between PET image slices with and without FDG-avid tumor lesions. Subsequently, the CAMs of this network were used to identify the tumor regions within images. This proposed framework was applied to FDG-PET/CT data of 453 oncological patients with available manually generated ground-truth segmentations. Quantitative segmentation performance was assessed for the different CAM approaches and compared with the manual ground truth segmentation and with supervised segmentation methods. In addition, further biomarkers (MTV and TLG) were extracted from the segmentation masks.Results: A weakly supervised segmentation of tumor lesions was feasible with satisfactory performance [best median Dice score 0.47, interquartile range (IQR) 0.35] compared with a fully supervised U-Net model (median Dice score 0.72, IQR 0.36) and a simple threshold based segmentation (Dice score 0.29, IQR 0.28). CAM, GradCAM++, and ScoreCAM yielded similar results. However, GradCAM led to inferior results (median Dice score: 0.12, IQR 0.21) and was likely to ignore multiple instances within a given slice. CAM, GradCAM++, and ScoreCAM yielded accurate estimates of metabolic tumor volume (MTV) and tumor lesion glycolysis. Again, worse results were observed for GradCAM.Conclusions: This work demonstrated the feasibility of weakly supervised segmentation of tumor lesions and accurate estimation of derived metrics such as MTV and tumor lesion glycolysis.

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Prognostic value of metabolic tumour volume and total lesion glycolysis measured by 18F‐fluorodeoxyglucose positron emission tomography/computed tomography in small cell lung cancer: A systematic review and meta‐analysis

Abstract: High MTV is associated with a significantly poorer prognosis OS and PFS, and high TLG is associated with a significantly poorer prognosis regarding OS for SCLC.

Cited by 10 publications

References 58 publications

mRNA Network: Solution for Tracking Chemotherapy Insensitivity in Small-Cell Lung Cancer

mRNA Network: Solution for Tracking Chemotherapy Insensitivity in Small-Cell Lung Cancer

PET with Different Radiopharmaceuticals in Neuroendocrine Neoplasms: An Umbrella Review of Published Meta-Analyses

Weakly supervised segmentation of tumor lesions in PET-CT hybrid imaging

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