Dual time point imaging for F18-FDG-PET/CT does not improve the accuracy of nodal staging in non-small cell lung cancer patients

Rogasch, J; Steffen, Ingo G.; Riedel, Sandra; Apostolova, Ivayla; Wertzel, H; Achenbach, H. Jost; Steinkrüger, Ferdinand L. G. A.; Kalinski, Thomas; Schultz, Meinald; Schreiber, Jens; Amthauer, Holger; Furth, Christian

doi:10.1007/s00330-015-4093-5

Cited by 8 publications

(12 citation statements)

References 21 publications

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“…Some reports have described visual scales used for interpreting lymph nodes on PET. [ 4 , 7 , 9 ] Interpretation of lesions on PET has been determined visually until now. Some recent reports have described the accuracy of visual evaluation as similar or superior to semi-quantitative SUV methods or DTPI.…”

Section: Discussionmentioning

confidence: 99%

“…Some recent reports have described the accuracy of visual evaluation as similar or superior to semi-quantitative SUV methods or DTPI. [ 4 , 7 ] As sSUVmax is considered a combination of visual interpretation and semi-quantitative SUV analyses, once visual analysis has been quantified, the results would be easily combined with sCT or any other method. Fourth, limit of this approach seemed to be the potential variability and poor reproducibility of this new parameter.…”

Section: Discussionmentioning

confidence: 99%

“…[ 2 ] Some reports, even from tuberculosis-endemic countries, have suggested that FDG-PET/CT could allow more accurate staging of RLNs than CT. [ 3 – 5 ] To improve diagnostic accuracy, thoracic clinicians such as respiratory oncologists and thoracic surgeons have considered simultaneous evaluation with FDG-PET/CT and contrast-enhanced CT [ 6 ] , and radiologists specializing in nuclear medicine have investigated methods such as dual time point PET imaging (DTPI), DTPI with a retention index, and visual analysis. [ 3 – 5 , 7 ] The clinical practical guidelines in NSCLC also described FDG-PET/CT as more sensitive than CT in identifying RLNs. [ 8 ] The FDG-PET/CT has been shown to be useful in restaging after adjuvant therapy.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have reported that the accuracy of semi-quantitative SUV analyses is no better than that of visual analysis. [ 4 , 7 ] Here we propose a “simplified SUVmax” (sSUVmax) using a color spectrum bar. The investigator determines the color highest in the ROI, and this is then easily quantified using the color bar as the sSUVmax.…”

Section: Introductionmentioning

confidence: 99%

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Clinically simplified screening methods to evaluate maximum standard uptake value from F-18-FDG-PET/CT in patients with non-small-cell lung cancer

Takeda

Miura²,

Sato³

et al. 2018

Medicine

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Maximum standard uptake value (SUVmax) of F-18-fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET/CT) is reportedly useful for evaluating regional lymph nodes (RLNs) of non-small-cell lung cancer (NSCLC) to predict malignancy. However, it is difficult for clinicians to measure SUVmax (mSUVmax) as calculated by a workstation.We assessed the utility of simplified SUVmax (sSUVmax) in screening RLNs for pathologic malignancy. The highest color was visually defined in the region of interest. The resulting color can be quantified using the color bar, and interpreted as sSUVmax. Patients in respiratory medicine and surgery who underwent both contrast-enhanced CT and FDG-PET/CT within 3 months before radical lobectomy were evaluated retrospectively. The correlation was examined by regression analysis and receiver operating characteristic (ROC) curve analyses.Participants comprised 69 patients with NSCLC treated between May 2009 and April 2016. Medical group comprised 22 patients from respiratory medicine. The prediction model could be written as a linear relationship (mSUVmax = 1.019 × sSUVmax; R2 = 0.930). A total of 316 RLNs resected by surgery in total cohort were pathologically determined. From ROC curves, area under curve for sSUVmax was 0.72 (95% confidence interval, 0.61–0.83; P < .0002). The cutoff sSUVmax was 2.42 (sensitivity, 52%; specificity, 88%; accuracy, 85%).The sSUVmax allows quantification of colors from FDG-PET/CT and shows a close correlation to mSUVmax. This value may have potential in screening for RLNs, and thoracic clinicians can readily determine the value. These findings may facilitate better planning of therapeutic strategy in the real world.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinically simplified screening methods to evaluate maximum standard uptake value from F-18-FDG-PET/CT in patients with non-small-cell lung cancer

Takeda

Miura²,

Sato³

et al. 2018

Medicine

View full text Add to dashboard Cite

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“…Dans une é tude portant sur 70 patients atteints d'un NSCLC, Kim et al ont montré que la SUVmax pré coce é tait plus performante que les paramè tres issus de l'acquisition DTP pour pré dire le statut N1 de la tumeur [44]. Dans une sé rie de 47 patients atteints d'un NSCLC, Rogasch et al ont montré que ni les images retardé es ni les paramè tres de l'acquisition DTP n'amé lioraient les performances de la TEP classique et de l'analyse visuelle pour le bilan d'extension ganglionnaire [45]. Pour conclure, dans une mé ta-analyse incluant 17 articles sur des tumeurs d'origines diffé rentes (8 pulmonaires, 2 mammaires, 2 oesophagiennes, 1 thyroïdienne, 1 ORL, 1 colorectale, 1 lymphome et 1 divers cancers), Shen et al ont conclu que les ré sultats ne soutiennent pas l'usage de l'acquisition DTP en routine clinique pour le diagnostic de ganglions mé tastatiques [46].…”

Section: Cancers Et Nodules Pulmonairesunclassified

TEP/TDM multi-phase : revue de la littérature

2021

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A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters

Rogasch

Michaels

Baumgärtner

et al. 2023

Eur J Nucl Med Mol Imaging

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Background In patients with non-small cell lung cancer (NSCLC), accuracy of [18F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment. Methods Monocentric retrospective analysis of pretherapeutic [18F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds (“test AUC”) and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%). Results N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87–0.94). Validation AUC was 0.94 (0.89–0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on “mediastinal LN uptake > mediastinum” (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM. Conclusions A machine learning model based on routinely available variables from [18F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.

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Dual time point imaging for F18-FDG-PET/CT does not improve the accuracy of nodal staging in non-small cell lung cancer patients

Abstract: • DTPI did not improve accuracy of PET-based LN staging in NSCLC. • Analyzed SUV ratios were not superior to LN SUVmax or SUVmean. • A four-step visual score may allow highly accurate, standardized LN assessment.

Cited by 8 publications

References 21 publications

Clinically simplified screening methods to evaluate maximum standard uptake value from F-18-FDG-PET/CT in patients with non-small-cell lung cancer

Clinically simplified screening methods to evaluate maximum standard uptake value from F-18-FDG-PET/CT in patients with non-small-cell lung cancer

TEP/TDM multi-phase : revue de la littérature

A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters

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