Every year, millions of people walk out of a radiology clinic carrying a diagnosis that is, in truth, a question mark: a small pulmonary nodule, a shadow on the lung measuring a few millimeters to a couple of centimeters, whose nature—benign scar or early cancer—cannot be determined from a CT scan alone. For these patients, the next step is often a positron emission tomography scan combined with computed tomography, known as ¹⁸F-FDG PET/CT, which tracks where a radioactive glucose analog accumulates in the body. Malignant cells, with their ravenous metabolism, typically gobble up more of the tracer than healthy tissue. But the picture is rarely black and white, and a long-standing debate has persisted in nuclear medicine: does scanning the patient a second time, hours after the first image, actually help distinguish cancer from non-cancer? A new study from Henan Provincial People’s Hospital in Zhengzhou, China, published in BMC Medical Imaging, offers one of the most methodically rigorous answers to date—and the answer is a cautious, quantified “only a little.”
The research team, led by Yang You, Weifeng Zhang, and colleagues in the hospital’s Department of Nuclear Medicine, set out to compare two statistical models for classifying indeterminate pulmonary nodules. The first, a conventional model, relied on seven predictors drawn from clinical data, CT features, and early-phase PET measurements. The second, an extended model, added a single extra variable: the retention index of maximum standardized uptake value, or RI-SUVmax, which captures how tracer concentration in the nodule changes between the early scan and a delayed scan performed roughly 120 to 180 minutes after injection of the radiopharmaceutical. The biological rationale is appealing. Inflammatory lesions and malignant tumors can both light up on early PET images, but the kinetics of glucose uptake differ; some investigators have argued that malignant tissue retains or accumulates FDG over time, while inflammatory activity tends to wash out. If true, delayed imaging could unmask cancers that the standard single-time-point scan misses.
To test this idea properly, the researchers assembled a cohort of 612 patients, each with a single index pulmonary nodule measuring between 8 and 30 millimeters. From an initial pool of 817 screened patients, 205 were excluded, leaving a study population in which every nodule eventually received a definitive reference diagnosis. Malignancy was confirmed in all cases by histopathology, obtained through surgical resection or image-guided biopsy. Benign status was established either by the same pathological route or, for solid nodules, by at least 24 months of serial chest CT follow-up showing stability or resolution. In total, 369 of the 612 patients—60.3 percent—turned out to have malignant nodules, a proportion that underscores just how high the stakes are in this diagnostic gray zone.
What distinguishes this study from much of the prior literature on dual-time-point PET imaging is its validation strategy. Rather than splitting patients randomly, the team assigned them chronologically: 312 patients scanned between 2018 and 2022 formed the training cohort, while 300 patients scanned between 2023 and 2025 formed a temporal validation cohort. This design matters because models that are tested on randomly shuffled data can quietly benefit from temporal leakage—subtle shifts in scanners, protocols, or patient demographics that make the test set resemble the training set. Temporal validation, by contrast, asks the harder question: does a model built on yesterday’s patients still work on tomorrow’s? The researchers also employed least absolute shrinkage and selection operator regression, or LASSO, to select predictors from a starting pool of 26 candidate variables, a technique that shrinks weak coefficients toward zero and helps prevent overfitting. Internal validation was performed with 1,000 bootstrap resamples, and the final locked models were then frozen and applied, unmodified, to the later cohort.
The headline result is a study in statistical nuance. In temporal validation, the conventional model achieved an area under the receiver operating characteristic curve of 0.891, with a 95 percent confidence interval of 0.854 to 0.928. The extended model, enriched with the delayed-phase retention index, reached 0.903, with an interval of 0.868 to 0.938. The paired difference between the two was just 0.0120, with a confidence interval stretching from −0.0003 to 0.0243 and a P value of 0.056—just shy of conventional statistical significance. In plain terms, adding the delayed scan produced a small improvement in the model’s ability to separate malignant from benign nodules, but the researchers could not confidently rule out that the difference was a fluke of this particular dataset. Brier scores, which combine discrimination and calibration into a single measure of predictive accuracy, were 0.134 for the conventional model and 0.127 for the extended one, again favoring the delayed approach modestly.
But the deeper story lies in the trade-offs and the calibration problem. At decision thresholds derived from the training data, the extended model pushed sensitivity up from 76.7 percent to 84.4 percent—meaning it caught more cancers—while specificity slipped from 86.7 percent to 81.7 percent, meaning more benign nodules were falsely flagged as suspicious. In a clinical setting, that shift translates into more patients with harmless lesions being funneled toward invasive biopsies or additional imaging, with all the anxiety, cost, and procedural risk those entail. Exploratory decision-curve analysis, which estimates the net benefit of acting on a model’s predictions across a range of risk thresholds, showed only threshold-dependent differences between the two approaches, leaving the clinical value of the extra scan contingent on how aggressively a given institution chooses to pursue nodules.
Perhaps the most sobering finding was the miscalibration. Calibration slopes in the temporal validation cohort were 0.557 for the conventional model and 0.482 for the extended model—far from the ideal value of 1.0. A perfectly calibrated model would assign a 70 percent malignancy probability to a group of nodules in which 70 percent are truly malignant; these models systematically overstated or understated risk when transplanted across time. The authors attribute this to the inherent difficulty of transporting prediction models across changing clinical eras and note that the miscalibration persisted regardless of whether delayed imaging was included. Their conclusion is unambiguous: recalibration and multicenter external validation are required before either model can be deployed in routine practice. This candor is refreshing in a field where promising discrimination statistics are sometimes oversold as clinical readiness.
The study also probed the robustness of its findings through a battery of exploratory analyses documented in supplementary materials. Subgroup analyses examined whether the benefit of delayed imaging varied by nodule characteristics, including a conventional definition of PET/CT-indeterminate status based on early SUVmax values between 1.5 and 4.0 or intermediate visual uptake scores. The team tested restrictions to a narrower 75-to-105-minute interval between early and delayed acquisitions, substituted delayed SUVmax for the retention index, and even refit the models using flexible restricted cubic splines to allow nonlinear relationships. Across these variations, the advantage of delayed imaging remained small and inconsistent, reinforcing the central message that the retention index is not a transformative addition to nodule characterization.
Why does this matter beyond the walls of one hospital in Zhengzhou? Dual-time-point PET imaging has been proposed, debated, and intermittently adopted for more than two decades, and the extra scan carries real costs: additional scanner time, higher radiation exposure from the second CT pass, greater patient burden, and scheduling complexity. If the incremental diagnostic yield is a statistically uncertain 0.012 in AUC, health systems may reasonably question whether the delayed phase earns its keep. At the same time, the study’s rigorous architecture—chronological validation, bootstrap internal checks, LASSO regularization, and transparent reporting of calibration failures—sets a template for how diagnostic prediction models should be evaluated before entering the clinic. As machine learning tools proliferate in medical imaging, studies like this one serve as a reminder that a model’s true test is not how impressively it fits the data it was trained on, but how faithfully it performs on patients it has never seen, months or years down the line.
For now, patients with indeterminate pulmonary nodules and their physicians are left with a nuanced picture. Early-phase ¹⁸F-FDG PET/CT, combined with clinical and CT features, remains a strong discriminator, achieving an AUC near 0.89 even without delayed imaging. The delayed scan may tip the balance in selected cases, particularly where sensitivity is paramount and a modest loss of specificity is acceptable, but the evidence does not yet justify routine adoption. The authors’ call for recalibration and multi-institutional validation is not a bureaucratic formality; it is the necessary next step before these models can safely inform decisions about who undergoes biopsy and who can be reassured with surveillance. In the ongoing effort to shrink the diagnostic gray zone of the solitary pulmonary nodule, this study measures the ground gained—and honestly reports how much of the fog remains.
Subject of Research: Diagnostic prediction modeling with early and delayed-phase ¹⁸F-FDG PET/CT for classifying indeterminate pulmonary nodules
Article Title: ¹⁸F-FDG PET/CT models with and without delayed-phase imaging for indeterminate pulmonary nodules: development and temporal validation
Article References: You, Y., Zhang, W., Zhen, Z., Xu, J., Hu, B., & Xuan, A. (2026). ¹⁸F-FDG PET/CT models with and without delayed-phase imaging for indeterminate pulmonary nodules: development and temporal validation. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02846-7
Image Credits: AI Generated
DOI: 10.1186/s12880-026-02846-7
Keywords: pulmonary nodules, ¹⁸F-FDG PET/CT, dual-time-point imaging, retention index, SUVmax, temporal validation, LASSO regression, lung cancer diagnosis, medical imaging, model calibration, nuclear medicine, diagnostic accuracy
News Source: Ophelia Keating. (October 9, 2026). Delayed PET Scans Offer Only Marginal Gains for Spotting Cancerous Lung Nodules. Scienmag.
