Writing in the open-access journal Heliyon, a team of pathologists and clinicians led by Van De Nguyen reports one of the first systematic analyses of invasive lobular carcinoma variants in a Vietnamese cohort, conducted according to the 2019 World Health Organization classification of breast tumors. Their central finding is striking: the pleomorphic variant of lobular carcinoma, marked by wildly abnormal nuclei and brisk cell division, acts as an independent predictor of axillary lymph node metastasis, the spread of cancer to the lymph nodes under the arm. That independence matters, because it means the variant signals danger even after accounting for tumor size and histological grade, the two conventional yardsticks oncologists reach for first.

The study examined 167 consecutive patients with primary invasive lobular carcinoma who underwent definitive breast surgery with axillary lymph node dissection at K Hospital’s Tan Trieu campus in Hanoi between January 2019 and July 2023. The researchers excluded mixed ductal-lobular tumors, pure in situ lesions, patients who had received neoadjuvant systemic therapy, and recurrent or metastatic disease. Crucially, rather than relying on prior diagnostic reports, two experienced breast pathologists independently re-reviewed hematoxylin and eosin stained sections from archival tissue blocks in a blinded fashion, reaching consensus diagnoses by joint re-review against WHO criteria. Where mixed growth patterns existed, the predominant pattern, occupying at least half of the invasive component, determined the variant assignment.

The cohort split into four groups: classic lobular carcinoma made up 53.9 percent of cases, the pleomorphic variant 22.8 percent, the solid variant 8.4 percent, and a composite category of rare variants, including alveolar, tubulo-lobular, histiocytoid, and signet-ring cell forms, 15 percent. That pleomorphic prevalence is remarkable. In most Western series, pleomorphic lobular carcinoma accounts for only a small minority of lobular cases, often between 2 and 4 percent and rarely exceeding 10 to 15 percent. The authors caution that their figure likely reflects referral bias at a tertiary oncology center, which attracts advanced and biologically aggressive tumors, rather than a true population-level difference, but the enrichment gave them statistical power to interrogate the variant’s behavior in detail.

The molecular portrait of the pleomorphic group diverged sharply from its classic counterpart. While 96.7 percent of classic tumors expressed the estrogen receptor and only 4.4 percent were HER2 positive, the pleomorphic tumors showed estrogen receptor positivity in just 63.2 percent of cases and HER2 positivity in 31.6 percent. Progesterone receptor loss followed the same trend, and the Ki67 proliferation index, a measure of how many tumor cells are actively dividing, averaged 28.2 percent in pleomorphic cases versus 19.3 percent in classic ones. Translated into molecular subtypes using St. Gallen criteria, half of classic tumors were Luminal A, the most indolent category, whereas the pleomorphic group was enriched for HER2-enriched and triple-negative phenotypes, which together made up more than a third of pleomorphic cases but were almost absent among classic tumors.

These molecular differences translated into clinical behavior. The pleomorphic variant was overwhelmingly high grade, with 71.1 percent of cases classified as Grade 3 under the Nottingham system, while not a single classic tumor reached that grade. Most tellingly, 84.2 percent of pleomorphic cases had already spread to axillary lymph nodes at surgery, compared with 58.9 percent of classic tumors and just 42.9 percent of solid variant tumors. Pleomorphic patients also carried the heaviest nodal burden, with 42.1 percent showing four or more involved nodes. On the Nottingham Prognostic Index, which combines tumor size, nodal stage, and grade into a single score, the pleomorphic group averaged 5.61, well into the poor-prognosis range above 5.4, and 65.8 percent of pleomorphic patients fell into the poor category, versus 15.6 percent of classic cases.

The statistical centerpiece of the study is a multivariable logistic regression model. In univariate analysis, larger tumor size, pleomorphic histology, and Grade 3 morphology were all associated with nodal spread. But when the variables were entered together, with the classic variant and Grade 1 as references, only tumor size and histological variant retained significance. Each additional centimeter of tumor raised the odds of nodal metastasis by 73 percent, while the pleomorphic variant carried roughly five times the odds of nodal involvement compared with classic lobular carcinoma, an odds ratio of 5.12 with a confidence interval of 1.11 to 23.62 and a p-value of 0.036. Grade, the traditional prognostic mainstay, lost its independent significance entirely once subtype was accounted for, a result the authors interpret as evidence that grading’s prognostic power in lobular carcinoma depends heavily on which variant is being graded.

The findings align with genomic studies showing that high-grade and pleomorphic lobular carcinomas accumulate dangerous alterations beyond the canonical 1q gain and 16q loss pattern of classic disease, including ERBB2 amplification, TP53 mutations, and complex copy number changes resembling those of high-grade ductal carcinomas. High Ki67 expression in hormone receptor positive disease has also been repeatedly linked to late recurrence and endocrine resistance. The practical implication is that patients with pleomorphic lobular carcinoma may be inadequately served by endocrine therapy alone and, when HER2 positive or otherwise high risk, may require chemotherapy and anti-HER2 targeted agents alongside carefully tailored endocrine strategies, consistent with current treatment guidelines for aggressive breast cancer subtypes.

Equally instructive is what the study found about the solid variant, which some earlier series had lumped with aggressive non-classic forms. In this cohort, solid-pattern tumors were predominantly intermediate grade, strongly hormone receptor positive, HER2 negative, and Luminal A-like, with the lowest nodal metastasis rate of any group and prognostic index scores comparable to classic disease. The authors suggest that solid-pattern lobular carcinoma lacking marked nuclear pleomorphism may behave as an indolent, endocrine-responsive cousin of classic ILC rather than a dedifferentiated high-grade lesion, though the subgroup was small at 14 patients and the conclusion awaits external validation. The study’s limitations, including its single-center retrospective design, modest numbers in rare variant groups, and the absence of long-term survival follow-up, temper firm conclusions. Still, the message for pathology practice is clear: reporting the specific histological variant of invasive lobular carcinoma, not just its grade, could identify high-risk patients who need more intensive axillary staging and systemic therapy, while sparing those with biologically indolent tumors from unnecessary treatment.

Subject of Research: Pleomorphic invasive lobular carcinoma as an independent predictor of axillary lymph node metastasis in breast cancer

Article Title: Pleomorphic variant as an independent predictor of axillary lymph node metastasis in invasive lobular carcinoma

Article References: Pleomorphic variant as an independent predictor of axillary lymph node metastasis in invasive lobular carcinoma. (n.d.). https://doi.org/10.1016/j.heliyon.2026.e45533

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45533

Keywords: invasive lobular carcinoma, pleomorphic variant, axillary lymph node metastasis, breast cancer, histological grading, HER2, Ki67 proliferation index, Nottingham Prognostic Index, molecular subtypes, WHO classification, Vietnam, pathology

The research team collected comprehensive data from 682 breast cancer patients diagnosed between 2014 and 2022 across four major hospitals in China. They compiled preoperative grayscale ultrasound (US), color Doppler flow imaging (CDFI), and contrast-enhanced ultrasound (CEUS) scans alongside critical clinical information. This rich dataset laid the groundwork for developing a multifaceted radiomics model aimed at detecting ALNM—a vital prognostic indicator that directly influences treatment decisions and survival outcomes in breast cancer patients.

Axillary lymph node involvement remains a pivotal determinant in breast cancer staging and therapy planning. Conventionally, detecting metastasis relies on invasive procedures like sentinel lymph node biopsy, often fraught with complications and patient burden. The advent of noninvasive radiomics models that extract quantitative imaging features from ultrasound modalities introduces a paradigm shift—enabling precise preoperative prediction and potentially reducing unnecessary surgical interventions.

Central to the study’s methodology was the application of Least Absolute Shrinkage and Selection Operator (LASSO) regression to distill a vast array of radiomic features embedded within ultrasound images. This statistical technique refined feature selection by eliminating redundancies and identifying those most predictive of ALNM. Subsequently, eight distinct machine learning algorithms were utilized to construct radiomics models drawing on US, CDFI, and CEUS datasets individually and in combination, providing a robust framework to evaluate predictive performance.

The results demonstrated that the combined US + CDFI + CEUS model significantly outperformed ultrasound-only assessments in predicting the presence and extent of metastatic axillary lymph nodes. Specifically, the integrated radiomics model achieved Areas Under the Curve (AUCs) of 0.88 in the training set and maintained strong predictive accuracy in both internal and external validation cohorts, with AUCs exceeding 0.75. These statistics underscore the reliability and generalizability of the approach across diverse clinical environments.

Beyond binary classification of lymph node status (N0 versus N+), the model adeptly distinguished between varying metastatic burdens—a crucial clinical nuance. It achieved high AUCs when differentiating patients with 1–2 positive nodes from those with three or more, highlighting its ability to stratify patients based on metastatic extent. This granularity facilitates more tailored therapeutic regimens, optimizing outcomes while minimizing overtreatment.

Importantly, the study also explored the prognostic implications of integrating radiomics with systemic immunoinflammatory markers, including platelet count and neutrophil-to-lymphocyte ratio (NLR). Such markers reflect tumor-host interactions and the underlying inflammatory milieu, which are increasingly recognized as key determinants of cancer progression. By combining the radiomic signature (Radscore) with these clinical biomarkers, the researchers developed a composite model predicting disease-free survival (DFS) with notable accuracy.

This composite clinical-radiomics model exhibited C-indices ranging from 0.73 to 0.80 across different cohorts, outperforming models based solely on clinical data. In external validations, it demonstrated superior AUCs for predicting 2-, 3-, and 5-year DFS outcomes compared to clinical models alone, with improvements reaching statistical significance. Such advances enable oncologists not only to forecast metastatic spread but also to anticipate patient prognosis, facilitating more informed counseling and individualized surveillance strategies.

The utility of the model was further bolstered by rigorous calibration and decision curve analyses, which confirmed its clinical applicability and net benefit. Good agreement between observed and predicted outcomes indicated that the model’s predictions closely mirrored real-world patient trajectories. Decision curve analysis demonstrated tangible clinical benefits over existing tools, suggesting that adopting this radiomics-based approach could enhance decision-making in breast cancer management.

At the core of the innovation lies the incorporation of contrast-enhanced ultrasound, which augments traditional imaging by highlighting microvascular perfusion characteristics within suspicious lesions and nodes. CEUS provides dynamic functional information beyond morphological assessment, enabling extraction of texture and intensity-based radiomic features deeply linked to tumor biology and metastatic potential. This multimodal imaging strategy thus enriches data granularity and model robustness.

The study’s multicenter design and large patient cohort contribute to the robustness and external validity of findings. Data partitioning ensured rigorous training, internal validation, and external validation phases, mitigating overfitting risks—a common pitfall in radiomics research. The diverse population sampled from geographically and institutionally distinct centers strengthens the generalizability of conclusions, paving the way for broader clinical adoption.

Nonetheless, the authors acknowledge that further prospective studies are warranted to validate the model’s performance in routine clinical workflows and to assess integration feasibility with existing diagnostic protocols. Future research may also explore combining ultrasound radiomics with genomic or molecular biomarkers, fostering an integrative oncology paradigm blending imaging phenotypes with biological insights.

Moreover, the technology holds promise for guiding neoadjuvant therapy decisions, surgical planning, and personalized follow-up schedules. By accurately delineating lymph node status preoperatively, the model may reduce the need for invasive biopsies, lowering patient morbidity and healthcare costs. Predicting prognosis through combined imaging and inflammatory markers offers a noninvasive avenue to identify high-risk patients who may benefit from intensified treatments.

This advancement epitomizes the transformative potential of artificial intelligence and machine learning in oncology, leveraging routinely acquired imaging data to extract clinically actionable insights. As ultrasound is widely available, radiation-free, and cost-effective, deploying enhanced radiomics models such as this could democratize access to precision diagnostics worldwide, especially in resource-limited settings.

The study epitomizes a confluence of medical imaging, computational analysis, and clinical oncology, showcasing how interdisciplinary efforts can unlock novel diagnostic and prognostic capabilities. By converting complex imaging data into predictive models that inform real-world treatment pathways, research like this accelerates the transition from traditional medicine to precision oncology.

In summary, the novel multimodal ultrasound radiomics model combining grayscale US, CDFI, and CEUS represents a formidable tool in the battle against breast cancer. It not only elevates the accuracy of axillary lymph node metastasis detection but also synergizes with immunoinflammatory biomarkers to enhance prognosis prediction. Such integrative models promise to optimize patient stratification, personalize therapy, and ultimately improve survival and quality of life for countless breast cancer patients worldwide.

As this technology matures and is incorporated into clinical practice, it may redefine breast cancer care pathways, reducing diagnostic uncertainties and empowering clinicians with nuanced risk assessments. The synergistic blend of advanced imaging, machine learning, and biological markers portends a new era of precision oncology where treatment decisions are increasingly guided by data-driven insights.

This landmark study stands as a testament to the potential of radiomics combined with immunology to revolutionize cancer prognostication. It paves the way for future endeavors harnessing artificial intelligence to unravel complex disease patterns from noninvasive imaging, transforming clinical oncology from reactive to proactive, and setting new standards in patient-centered care.

Subject of Research:
Prediction of axillary lymph node metastasis and prognosis in breast cancer using multimodal ultrasound radiomics combined with immunoinflammatory markers.

Article Title:
Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study.

Article References:
Li, S.Y., Li, Y.M., Fang, Y.Q. et al. Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study. BMC Cancer 25, 1315 (2025). https://doi.org/10.1186/s12885-025-14632-9

Image Credits: Scienmag.com

DOI:
https://doi.org/10.1186/s12885-025-14632-9