The dynamics and prognostic value of FDG PET-metrics in weekly monitoring of (chemo)radiotherapy for NSCLC

Fontaine, Matthew D. La; Bruin, N. M.W.J de; Kranen, S. Van; Knegjens, J.; Kamer, Jeroen B. van de; Vogel, Wouter V.; Sonke, Jan‐Jakob

doi:10.1016/j.radonc.2021.04.009

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…Our findings support a predictive value of baseline SUV peak . Nevertheless, the best time point for response assessment is an area often discussed by experts, and studies investigating the optimal timepoint for response assessment during cCRT for patients with LA-NSCLC have found that both different timepoints and the longitudinal response-measurements had high prognostic value [14] , [15] .…”

Section: Discussionmentioning

confidence: 99%

“…Patient selection and eligibility are currently based on clinical baseline data such as stage and performance status (PS). However, several studies have suggested that early radiologic (CT-based) and metabolic (positron emission tomography (PET)-based) response to treatment might add predictive value, and a variety of metrics at different timepoints of the treatment course have been investigated [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] . Unfortunately, most studies are limited by small patient numbers, different timepoints of measuring response, incompatible endpoints, and conflicting results.…”

Section: Introductionmentioning

confidence: 99%

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Early radiologic and metabolic tumour response assessment during combined chemo-radiotherapy for locally advanced NSCLC

Tvilum,

Knap,

Hoffmann

et al. 2024

Clinical and Translational Radiation Oncology

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Early radiologic and metabolic tumour response assessment during combined chemo-radiotherapy for locally advanced NSCLC

Tvilum,

Knap,

Hoffmann

et al. 2024

Clinical and Translational Radiation Oncology

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“…This result confirms that semiquantitative parameters, even if less accurate, well represent the glucose metabolism only if all technical and procedural aspects in patient preparation and scan acquisition are strictly respected. Indeed, especially when several PET/CT examinations are repeated over time for the evaluation of response to oncological treatment (44)(45)(46), the difficulty in strictly respecting all technical aspects can make the semiquantitative parameters unreliable. Moreover, it is important to remind that the accuracy of semiquantitative parameters may be affected by non-controlled aspects such as the oncological therapies that can modify the tracer bioavailability (especially the news antiangiogenic drugs), the cancer cells biological characteristics (tracer uptake), and other unknown biologic and patients factors (47,48).…”

Section: Discussionmentioning

confidence: 99%

Short 2-[18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer

et al. 2021

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Purpose: To test a short 2-[18F]Fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET dynamic acquisition protocol to calculate Ki using regional Patlak graphical analysis in patients with non-small-cell lung cancer (NSCLC).Methods: 24 patients with NSCLC who underwent standard dynamic 2-[18F]FDG acquisitions (60 min) were randomly divided into two groups. In group 1 (n = 10), a population-based image-derived input function (pIDIF) was built using a monoexponential trend (10–60 min), and a leave-one-out cross-validation (LOOCV) method was performed to validate the pIDIF model. In group 2 (n = 14), Ki was obtained by standard regional Patlak plot analysis using IDIF (0–60 min) and tissue response (10–60 min) curves from the volume of interests (VOIs) placed on descending thoracic aorta and tumor tissue, respectively. Moreover, with our method, the Patlak analysis was performed to obtain Ki,s using IDIFFitted curve obtained from PET counts (0–10 min) followed by monoexponential coefficients of pIDIF (10–60 min) and tissue response curve obtained from PET counts at 10 min and between 40 and 60 min, simulating two short dynamic acquisitions. Both IDIF and IDIFFitted curves were modeled to assume the value of 2-[18F]FDG plasma activity measured in the venous blood sampling performed at 45 min in each patient. Spearman's rank correlation, coefficient of determination, and Passing–Bablok regression were used for the comparison between Ki and Ki,s. Finally, Ki,s was obtained with our method in a separate group of patients (group 3, n = 8) that perform two short dynamic acquisitions.Results: Population-based image-derived input function (10–60 min) was modeled with a monoexponential curve with the following fitted parameters obtained in group 1: a = 9.684, b = 16.410, and c = 0.068 min−1. The LOOCV error was 0.4%. In patients of group 2, the mean values of Ki and Ki,s were 0.0442 ± 0.0302 and 0.33 ± 0.0298, respectively (R2 = 0.9970). The Passing–Bablok regression for comparison between Ki and Ki,s showed a slope of 0.992 (95% CI: 0.94–1.06) and intercept value of −0.0003 (95% CI: −0.0033–0.0011).Conclusions: Despite several practical limitations, like the need to position the patient twice and to perform two CT scans, our method contemplates two short 2-[18F]FDG dynamic acquisitions, a population-based input function model, and a late venous blood sample to obtain robust and personalized input function and tissue response curves and to provide reliable regional Ki estimation.

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The dynamics and prognostic value of FDG PET-metrics in weekly monitoring of (chemo)radiotherapy for NSCLC

Cited by 2 publications

References 31 publications

Early radiologic and metabolic tumour response assessment during combined chemo-radiotherapy for locally advanced NSCLC

Early radiologic and metabolic tumour response assessment during combined chemo-radiotherapy for locally advanced NSCLC

Short 2-[18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer

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