The study, published in BMC Medical Imaging, was led by Seckin Bilgic and colleagues at the Department of Nuclear Medicine of Bursa Uludag University, with additional contributions from Canakkale Onsekiz Mart University and Bursa Yuksek Ihtisas Training and Research Hospital. The team set out to answer a deceptively simple question: when adults undergo fluorine-18 fluorodeoxyglucose positron emission tomography and computed tomography, known as 18F-FDG PET/CT, for cancer evaluation, how often do their kidneys show abnormalities, and how often do those abnormalities actually make it into the written report? The answer, according to their data, is that kidney findings are remarkably common and remarkably under-reported.

The researchers examined 283 consecutive adult patients who underwent oncologic 18F-FDG PET/CT at their institution between March and April 2023. Consecutive enrollment matters here, because it means the cohort was not cherry-picked; these were simply the patients who came through the door during a defined two-month window. The study received ethical approval from the Bursa Uludag University Ethics Research Committee and was conducted in accordance with the 1964 Helsinki Declaration, with informed consent obtained from all subjects. Because the design was retrospective and observational, the investigators could go back and scrutinize the images with a level of systematic attention that busy clinical workflows do not always permit.

What did that scrutiny reveal? The numbers are eye-opening. At least one cortical or pelvicalyceal renal abnormality was identified in 192 of the 283 patients, which works out to 67.8 percent of the cohort, with a 95 percent confidence interval spanning 62.2 to 73.0 percent. In other words, roughly two out of every three cancer patients scanned carried some detectable kidney abnormality on their PET/CT images. The abnormalities fell into two broad morphological categories. Cortical abnormalities, which affect the outer functional tissue of the kidney, were present in 123 patients, or 43.5 percent, and were most commonly large cortical cysts, seen in 22.3 percent of the full cohort. Pelvicalyceal abnormalities, which involve the urine-collecting system deep within the kidney, appeared in 129 patients, or 45.6 percent.

The technical distinction between these two categories is worth unpacking, because it reflects the dual nature of the information contained in a PET/CT scan. The CT component provides high-resolution anatomical detail without contrast enhancement in this setting, allowing reviewers to assess the renal cortex for cysts, masses, and structural changes. The pelvicalyceal system, meanwhile, can be evaluated for dilatation, stones, and other collecting-system pathology. The researchers even developed a stepwise visual grading algorithm, described in their supplementary material, to classify pelvicalyceal dilatation from Grade 0 to Grade 3 based on renal pelvic fullness, calyceal dilatation, and associated thinning of the renal parenchyma. Notably, they used the fused PET/CT images only for anatomical correlation and deliberately ensured that the intense urinary excretion of FDG, which naturally accumulates in the collecting system, did not influence grade assignment. That methodological care matters, because urinary FDG activity is a well-known confounder that can obscure or mimic collecting-system findings.

But prevalence alone was not the heart of the study. The investigators also wanted to know whether the kidney findings they identified on systematic review had ever been documented before. For the 192 patients with at least one renal abnormality, they reviewed available pre-index institutional records, meaning prior imaging and reports already on file at the institution, and classified each patient’s findings as previously documented, mixed, or incidentally identified. The result was stark: only 20 patients, or 10.4 percent, had findings that were entirely previously documented. Another 31 patients, or 16.1 percent, had a mixed picture, with some findings known and some new. The remaining 141 patients, a full 73.4 percent, had kidney abnormalities that were entirely new discoveries, never before captured in the institutional record. Taken together, 172 of the 192 affected patients, or 89.6 percent, had at least one renal abnormality that had never been documented anywhere in their available records.

The most sobering comparison, however, came when the researchers turned to the original clinical PET/CT reports, the documents written by the interpreting physicians at the time of the scan and sent to the treating oncologists. Of the 192 patients whose scans showed renal abnormalities on standardized retrospective review, only 58, or 30.2 percent, had at least one of those abnormalities mentioned in the original report. The other 134 patients, 69.8 percent of those with findings, had no renal abnormality documented at all. The gap between what a systematic review could see and what the clinical report conveyed is the central finding of the paper, and it is difficult to read those numbers without wondering how many clinically meaningful findings slip through the cracks in routine practice.

Why does this happen? The authors point to the inherent structure of oncologic imaging interpretation. A PET/CT study ordered for cancer staging or surveillance generates hundreds of images, and the interpreting physician’s primary obligation is to the clinical question at hand: tumor location, metabolic activity, treatment response, and new metastatic disease. Kidney cysts and mild collecting-system dilatation are, in the vast majority of cases, benign and clinically silent, and flagging every one of them in every report would risk burying the critical oncologic information in a flood of incidental detail. This is the classic tension of incidental findings in modern imaging: as scanners have grown more sensitive and datasets more voluminous, the sheer volume of non-target abnormalities has outpaced any practical reporting strategy. Yet the study’s data suggest the pendulum may have swung too far, with nearly 90 percent of affected patients carrying at least one renal finding absent from all available documentation.

The clinical stakes are not trivial. Some incidental renal abnormalities, particularly complex cysts and solid-appearing cortical lesions, can represent early renal cell carcinoma, a malignancy that is highly curable when caught at a small size but far more dangerous when it grows undetected. Hydronephrosis and collecting-system dilatation can signal obstructing stones or tumors that merit follow-up. Chronic kidney disease, too, often announces itself first through subtle structural changes visible on cross-sectional imaging. Whether the abnormalities catalogued in this study would have changed outcomes for any individual patient is a question the retrospective design cannot answer, and the authors are appropriately measured on this point. They explicitly state that further studies should determine whether more systematic recognition and reporting of these findings influences diagnostic evaluation, clinical decision-making, and patient management.

Still, the study lands at a moment when the imaging community is actively grappling with how to handle the incidentalome, the ever-growing list of unexpected findings generated by modern scans. Structured reporting templates, standardized checklists for organ systems outside the region of interest, and artificial intelligence tools that pre-screen images for non-target abnormalities are all under active investigation as potential solutions. This study provides a concrete baseline for one organ system in one high-volume imaging context, and its message is clear: kidney abnormalities are present in more than two-thirds of oncologic PET/CT patients, yet nearly 70 percent of affected patients leave the encounter with none of those findings on paper. Closing that gap, without drowning the oncologic signal in incidental noise, may be one of the defining reporting challenges of the PET/CT era. The authors received no specific funding for the work and declared no competing interests, and the article is published open access under a Creative Commons license, making the full data and methods available to any research group hoping to replicate or extend the findings.

Subject of Research: Prevalence and under-reporting of incidental renal abnormalities detected on oncologic 18F-FDG PET/CT imaging

Article Title: Incidental renal abnormalities on 18F-FDG PET/CT in oncologic patients: prevalence and reporting practices

Article References: Bilgic, S., Koroglu, I., Akovalı, B., Sahin, H. G., & Alper, E. (2026). Incidental renal abnormalities on 18F-FDG PET/CT in oncologic patients: prevalence and reporting practices. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02859-2

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02859-2

Keywords: 18F-FDG PET/CT, incidental findings, renal abnormalities, oncologic imaging, kidney cysts, pelvicalyceal dilatation, reporting practices, nuclear medicine, radiology, renal cell carcinoma, patient safety, medical imaging

Researchers led by Hongji Zhu and Wei Wei at the First Affiliated Hospital of the University of Science and Technology of China set out to determine whether dual-energy CT, a technology that captures images at two different X-ray energy levels simultaneously, could reveal hidden signatures of lymph node metastasis in pancreatic cancer. Their investigation enrolled 126 patients with pathologically confirmed pancreatic ductal adenocarcinoma, dividing them into a training cohort of 87 patients and an independent test cohort of 39 patients. The retrospective design, approved by the hospital’s ethics committee and conducted in accordance with the Declaration of Helsinki, allowed the team to mine a rich trove of imaging and clinical data that had already been collected during routine care.

The technological foundation of the study deserves careful explanation, because it is precisely what gives the approach its power. Conventional CT scans produce a single grayscale image that reflects how much X-ray radiation is absorbed by tissue, a measurement that cannot distinguish between materials that attenuate radiation to a similar degree. Dual-energy CT, by contrast, acquires data at two distinct photon energies, enabling the reconstruction of material-specific information such as iodine concentration, effective atomic number, and the slope of the energy attenuation curve, commonly denoted by the Greek letter lambda. This spectral slope quantifies how rapidly a tissue’s attenuation changes as the X-ray energy decreases, a property that is exquisitely sensitive to the microstructural and compositional characteristics of tissue, including the density of blood vessels, the degree of fibrosis, and the presence of necrotic or proliferative regions within a tumor.

Armed with these spectral parameters, the researchers systematically searched for the variables that best separated patients whose cancer had spread to lymph nodes from those whose disease remained localized. Through univariate and multivariate logistic regression analyses, three independent predictors emerged from the statistical gauntlet. The first was CA19-9, a carbohydrate antigen that has long served as the workhorse tumor marker in pancreatic cancer, rising in the blood as tumor burden increases. The second was intratumoral necrosis, the death of tissue within the tumor core, which reflects the aggressive, oxygen-starved biology of fast-growing cancers. The third was the slope of the energy attenuation curve measured during the venous phase of contrast enhancement, a dual-energy CT parameter that captures how the tumor takes up and retains iodinated contrast material in its vascular supply.

Individually, each of these predictors tells only part of the story. CA19-9 can be elevated for reasons unrelated to metastasis, including biliary obstruction and inflammation, which are common complications of pancreatic tumors. Intratumoral necrosis can be difficult to grade consistently on conventional images. Spectral parameters, while objective, reflect tissue properties rather than anatomy directly. It is the fusion of all three into a single predictive model that produces the leap in diagnostic power, because the weaknesses of each variable are compensated for by the strengths of the others. This is the central logic of the combined model that the team constructed and validated across their two patient cohorts.

The performance numbers, when they arrived, were impressive by the standards of preoperative oncologic imaging. In the training cohort, the combined model achieved an area under the receiver operating characteristic curve of 0.816, with a 95 percent confidence interval spanning 0.730 to 0.874. In the independent test cohort, the model held its ground with an AUC of 0.761, with a confidence interval of 0.613 to 0.909. An AUC of this magnitude indicates that the model discriminates between metastatic and non-metastatic patients substantially better than chance and better than many existing clinical assessments alone. Equally important was the demonstration that the model’s predicted probabilities matched observed outcomes, a property assessed through calibration curves and the Hosmer-Lemeshow test, which yielded non-significant P values of 0.722 and 0.604 in the training and test cohorts respectively. Good calibration means that when the model says a patient has a 70 percent chance of nodal involvement, roughly 70 percent of such patients genuinely do.

Beyond discrimination and calibration, the researchers subjected their model to decision curve analysis, a technique that quantifies the net clinical benefit of acting on a model’s predictions across the full spectrum of decision thresholds. The analysis confirmed that the combined model delivered superior net benefit within threshold probabilities of 0 to 0.78 in the training cohort and 0 to 0.80 in the test cohort, meaning that clinicians using the model to guide decisions would enjoy better outcomes across a wide and clinically relevant range of risk tolerances. This is not an academic nicety; decision curve analysis exists precisely to answer the question of whether a model is actually useful in the messy reality of clinical practice, where false positives lead to unnecessary interventions and false negatives to missed opportunities for aggressive treatment.

The clinical implications of this work ripple outward in several directions. For surgeons, an accurate preoperative estimate of lymph node metastasis could inform the extent of lymphadenectomy, the completeness of which is known to influence survival. For oncologists, patients identified as high risk before surgery might be steered toward neoadjuvant therapy, the strategy of treating the tumor with chemotherapy or radiation before resection, which is increasingly favored for borderline and locally advanced disease. For patients and families, a quantitative, individualized risk estimate replaces vague categorizations with something concrete that can anchor shared decision-making. Because dual-energy CT is already deployed in many major medical centers and requires no additional radiation beyond a standard contrast-enhanced examination, the barrier to real-world adoption is remarkably low compared with emerging technologies that demand new equipment or invasive biopsies.

It is worth acknowledging the study’s limitations honestly, as the authors themselves imply through their careful design. The cohorts were retrospective and relatively modest in size, and the model was validated internally through a train-test split rather than through a fully external, multicenter validation. Prospective studies across multiple institutions with diverse patient populations will be needed before the model can be endorsed for universal clinical use. Nevertheless, the conceptual achievement stands on its own: three independent, biologically grounded predictors, one drawn from blood, one from tumor morphology, and one from the spectral physics of dual-energy imaging, converge into a tool that sees what conventional staging cannot. In a disease where every week matters and where treatment decisions cascade from the first imaging study, that is a genuinely meaningful advance.

The study, supported by the National Natural Science Foundation of China and partner institutions including GE HealthCare’s CT Imaging Research Center, arrives at a moment when the field of oncologic imaging is undergoing a quiet revolution. Spectral imaging, radiomics, and artificial intelligence are converging to extract ever more information from scans that patients already undergo as part of standard care. This research exemplifies that convergence in one of medicine’s most unforgiving arenas, demonstrating that the physics of X-ray attenuation, properly harnessed and combined with clinical chemistry, can peer into the biology of pancreatic cancer and reveal whether it has already begun its deadly migration. For the roughly half million people diagnosed with pancreatic cancer each year worldwide, tools like this one represent something rare and precious in the fight against this disease: clarity, delivered earlier, from a scan they were going to have anyway.

Subject of Research: Predicting lymph node metastasis in pancreatic ductal adenocarcinoma using dual-energy CT multiparameters combined with clinical indicators

Article Title: Model construction and validation of dual-energy CT multi-parameters combined with clinical indicators for predicting lymph node metastasis in pancreatic ductal adenocarcinoma

Article References: Model construction and validation of dual-energy CT multi-parameters combined with clinical indicators for predicting lymph node metastasis in pancreatic ductal adenocarcinoma. (n.d.). https://doi.org/10.1186/s12880-026-02765-7

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02765-7

Keywords: pancreatic ductal adenocarcinoma, dual-energy CT, lymph node metastasis, predictive model, CA19-9, energy attenuation curve, BMC Medical Imaging, oncologic imaging, radiology, cancer staging, decision curve analysis, predictive medicine