Every tumor is a landscape. Within a single breast cancer, some neighborhoods bristle with immune cells poised to attack, while others are dominated by spindle-shaped stromal cells that quietly remodel the tissue around them. For decades, oncologists have wondered whether this internal diversity leaves a fingerprint on the images clinicians see every day. A new exploratory study, published in BMC Medical Imaging, has now taken one of the most direct looks yet at that question, pairing machine-learning analysis of tumor tissue with the metabolic maps produced by 18F-FDG positron emission tomography, the workhorse scan of modern cancer imaging.
The research, conducted by Berkay Çağdaş of the Department of Nuclear Medicine at Afyonkarahisar State Hospital and Ege University’s Institute of Nuclear Sciences, and Elif Kardelen Çağdaş of the hospital’s Department of Pathology and Ankara University’s Graduate School of Biotechnology, set out to determine whether regional differences in immune and stromal composition inside breast tumors correspond to measurable differences in how the tumor consumes glucose on PET imaging. The premise is biologically plausible: tumors infiltrated by lymphocytes often behave differently from immune-cold tumors, and fibroblast-like stromal cells can alter metabolism, perfusion, and tissue architecture in ways that might plausibly change fluorodeoxyglucose uptake.
The study enrolled 61 women with invasive breast carcinoma who underwent baseline 18F-FDG PET/CT before treatment. Rather than treating each tumor as a single homogeneous blob, the team took a deliberately regional approach. A pathologist, blinded to all PET findings, annotated ten separate intratumoral regions on whole-slide histopathology images, each at least one square millimeter in area. QuPath, an open-access machine-learning platform for digital pathology, then quantified two key features within each region: the density of stromal tumor-infiltrating lymphocytes, the immune cells that gather in the connective tissue surrounding cancer cells, and the density of fibroblast-like stromal cells, spindle-shaped cells identified morphologically on routine hematoxylin and eosin stains.
From those ten regions, the investigators retained the ones with the numerically highest and lowest lymphocyte density for paired analysis, an approach designed to capture the observed range of immune-stromal composition within each tumor. The authors are careful to note that these paired regions represent the extremes of what was measured, not a comprehensive spatial map of every tumor. On the imaging side, the team extracted a rich panel of 30 PET features from each patient’s scan. These included the familiar intensity measures such as maximum, mean, and peak standardized uptake values, volumetric measures including metabolic tumor volume and total lesion glycolysis, texture descriptors derived from gray-level co-occurrence, run-length, and size-zone matrices, and dedicated heterogeneity indices, including the HI2 and HI3 indices and a slope calculated across metabolic volume thresholds from 30 to 50 percent of maximum uptake.
The statistical framework was deliberately conservative. The team ran Spearman correlations between nine immune-stromal endpoints and all 30 PET features, generating 270 individual tests, and then applied Benjamini-Hochberg correction to control the false discovery rate, a standard safeguard against being fooled by chance when many comparisons are made simultaneously. The results were sobering but informative. Thirty-eight of the 270 correlations reached nominal significance at the conventional p-value threshold of 0.05, yet not a single one survived the false discovery rate correction, whether the correction was applied within each endpoint family or across the entire study. The smallest corrected q-value within an endpoint was 0.077, and study-wide it was 0.33, both well above any threshold that would conventionally be called significant.
The strongest raw association was, by the authors’ own framing, weak. The density of stromal tumor-infiltrating lymphocytes in the TIL-rich region correlated with the HI2 heterogeneity index at a Spearman coefficient of 0.379 in absolute value, with a 95 percent confidence interval spanning 0.126 to 0.586 and a nominal p-value of 0.0026. Beyond that single headline number, a coherent pattern emerged in the direction of the nominal findings. Lymphocyte density correlated inversely with metabolic tumor volume, with coefficients ranging from negative 0.29 to negative 0.35, and positively with the HI2 heterogeneity measure. The ratio of lymphocytes to fibroblast-like stromal cells correlated with SUV-based parameters, the largest being a coefficient of 0.299 with mean standardized uptake value. Fibroblast density on its own, and the metrics describing regional differences between the paired areas, produced weaker and less consistent signals.
One of the study’s most technically rigorous contributions is its validation of the pathology measurement itself. Because fibroblast-like stromal cells were defined morphologically on routine H&E stains, the authors cross-checked their machine-learning quantification against alpha-smooth muscle actin immunohistochemistry, a stain that highlights activated fibroblasts and myofibroblasts, in the same 122 paired regions. The agreement was remarkably tight: a correlation of 0.992 and a Lin concordance correlation coefficient of 0.996. When the entire correlation analysis was repeated using the immunohistochemistry-based stromal density instead of the H&E-based one, the profile of results was reproduced almost exactly, with correlations between the two analyses ranging from 0.94 to 0.996. This means the null finding is not easily explained by measurement error in the pathology pipeline.
The team also stress-tested the associations that did reach nominal significance. When extreme lymphocyte densities or the smallest annotated regions were excluded from the analysis, between 9 and 24 of the original 38 nominal associations persisted, indicating that some of the signals were fragile and dependent on particular data points. After statistical adjustment for clinical covariates including T stage, Nottingham histologic score, Ki-67 proliferation index, and molecular subtype, only 13 of the 38 associations remained nominally significant. The picture that emerges is one of faint, directionally interesting signals that dissolve under the weight of proper multiple-comparison correction, exactly the pattern one expects when exploring a genuinely uncertain hypothesis with a modest sample size.
Why does this matter? PET radiomics has generated enormous enthusiasm in oncology, with hundreds of studies proposing imaging texture features as noninvasive surrogates for everything from gene expression to immune infiltration. The promise is seductive: if a scan could reveal the immune landscape of a tumor without a biopsy, clinicians could select patients for immunotherapy, track treatment response, and monitor clonal evolution noninvasively. But the field has been repeatedly criticized for underpowered studies, lax statistical correction, and features that fail to replicate. This study is a refreshing counterexample. By pre-specifying a conservative statistical framework, validating its pathology measurements against an independent stain, and openly reporting that nothing survived correction, the authors have modeled the kind of intellectual honesty that imaging science needs more of.
The authors themselves are unambiguous about the limits of their work. This is a hypothesis-generating, exploratory, single-institution study of 61 patients, and the paired-region design captures only the extremes of immune-stromal composition rather than a full spatial map. They state plainly that the findings require independent validation in larger, multi-institutional cohorts before any biological or clinical interpretation can be drawn. That caution is warranted, but so is the value of the attempt. The study demonstrates a feasible, reproducible workflow for integrating region-level digital pathology with PET radiomics in breast cancer, and it establishes a realistic benchmark for how weak the true links between tissue microenvironment and metabolic imaging may actually be. If future larger studies confirm even the modest directional trends seen here, immune-rich tumors may one day be recognizable on PET as smaller, more heterogeneous glucose-avid lesions. For now, the honest answer to the question of whether a PET scan can see a tumor’s immune landscape is: perhaps faintly, and not yet reliably.
Subject of Research: Associations between regional immune-stromal tumor features and 18F-FDG PET metabolic heterogeneity in breast cancer
Article Title: Exploratory associations between regional immune-stromal features and 18F-FDG PET-derived metabolic heterogeneity in breast cancer
Article References: Çağdaş, B., & Çağdaş, E. K. (2026). Exploratory associations between regional immune-stromal features and 18F-FDG PET-derived metabolic heterogeneity in breast cancer. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02789-z
Image Credits: AI Generated
DOI: 10.1186/s12880-026-02789-z
Keywords: breast cancer, PET imaging, radiomics, tumor microenvironment, tumor-infiltrating lymphocytes, digital pathology, metabolic heterogeneity, 18F-FDG, QuPath, fibroblasts, false discovery rate, machine learning
News Source: Nathaniel Bowman. (October 7, 2026). PET Scans and Tumor Immunity: New Study Probes the Hidden Link in Breast Cancer. Scienmag.
