Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph

Lung cancer remains a leading cause of global cancer mortality, demanding precise diagnostic tools for accurate subtype classification. This paper introduces a novel Enhanced GraphSAGE (E-GraphSAGE) framework that integrates graph-based deep learning (GBDL) with traditional image processing to classify lung cancer subtypes—Adenocarcinoma (ACA), Squamous Cell Carcinoma (SCC), and Benign Tissue (BNT)—from H&E-stained Whole-Slide Images (WSIs). Our methodology leverages Gray-Level Co-occurrence Matrix (GLCM) features to quantify tissue texture, constructs a Sparse Cosine Similarity Matrix (SCSM) to model spatial relationships, and employs DeepWalk embeddings to capture topological patterns. The E-GraphSAGE architecture optimizes neighborhood aggregation, incorporates dropout regularization to mitigate overfitting, and utilizes Principal Component Analysis (PCA) for dimensionality reduction, ensuring computational efficiency without sacrificing diagnostic fidelity. The model is validated on multicell Lymphocytic cancer classification of Diffuse Large B-cell lymphoma (DLBCL), Follicular Lymphoma (FL) and Small Lymphocytic Lymphoma (SLL), experimental results demonstrate superior performance, achieving 96% training accuracy and 90% validation accuracy, with an F1-score of 0.91 and AUC-ROC of 0.95 (DLBCL), 0.92 (FL), and 0.89 (SLL). Comparative analysis against state-of-the-art models (GAT, GCN, ResNet-50, ViT) reveals our framework’s dominance, attaining an overall accuracy of 0.90, F1-score of 0.905, and macro-average AUC-ROC of 0.93. While maintaining 25.7 sec/slide inference speed—significantly faster than competing methods. This study advances computational pathology by unifying Graph Neural Networks (GNN) with interpretable feature engineering, offering a scalable, efficient solution for cancer subtype classification. The framework’s ability to model multi-scale histopathological patterns—from cellular interactions to tissue architecture—positions it as a promising tool for clinical decision support, enhancing diagnostic precision and patient outcomes in hemato-pathology.

1 Introduction

Lung cancer remains one of the most lethal cancers globally, responsible for approximately 1.8 million deaths each year (1). A major contributing factor to this high mortality rate is the prevalence of late-stage diagnoses (2). Current diagnostic methods relying on manual pathological examination present several critical challenges: time-consuming, inherently subjective, and suffer from significant inter-observer variability (3). These limitations underscore the demanding for developing automated, high-precision computational tools to support pathologists in clinical decision-making.

The diagnostic challenge becomes particularly complex when distinguishing between the three key histological subtypes—ACA, BNT and SCC and other carcinomas (e.g., neuroendocrine tumors)—each exhibiting subtle but clinically significant morphological differences. Recent advances in Machine Learning (ML) have demonstrated remarkable potential in addressing these complex classification problems, offering accurate and reproducible solutions that augment traditional histopathological analysis. For instance, Deep Learning (DL) models, such as residual networks, have achieved high accuracy in differentiating lung ACA from SCC by analyzing histopathological images (4). These computational approaches are transforming cancer diagnostics by providing objective, quantitative assessments of tissue architecture and cellular morphology, complementing conventional microscopy-based evaluation (5). Studies have further shown that ML-based methods enhance diagnostic reproducibility by detecting subtle morphological variations that may be overlooked in manual examination (6).

The advancements in DL have demonstrated remarkable success in Medical Image Analysis (MIA), particularly Convolution Neural Networks (CNNs) for tumor detection (7). Yet, CNNs face critical limitations in histopathology, where spatial relationships between tissue regions such as glandular formations in ACA or keratinized nests in SCC are diagnostically decisive but poorly captured by grid-based convolutions (8). However, such problems are being solved by nuclear feature extraction, that has successfully addressed a wide range of pathology applications, including nucleus segmentation, tissue segmentation, nuclei categorization (9), tumor identification (10) and staging (11). Traditional ML models, such as Support Vector Machines (SVM) (12) and CNN (13), have been widely employed for this purpose. However, these models often face challenges in effectively capturing the complex spatial dependencies inherent in tissue samples, which are crucial for accurate classification of complex structure of pathology images.

In recent years, GNN is introducing new techniques to cope with the complex structures like histopathology images when classifying multi-class tissue structures (14). Specifically, GraphSAGE, offer a promising alternative by modelling WSIs as topological graphs, where nodes represent tissue patches and edges encode structural dependencies (15). However, conventional GNNs struggle with computational inefficiency and loss of fine-grained morphological details when processing large-scale histopathology datasets (16).

To address these challenges, we propose an E-GraphSAGE framework that synergizes traditional texture analysis with GBDL for robust lung cancer subtyping. Our methodology introduces three key innovations:

1. Multi-scale feature extraction using GLCM to quantify tumor heterogeneity, followed by sparse graph construction via SCSM, preserving only biologically relevant tissue interactions.

2. Unsupervised DeepWalk embeddings to encode global tissue architecture, E-GSAGE to discern diagnostically critical patterns (e.g., ACA’s glandular disarray vs. SCC’s keratin pearls).

3. Optimized neighborhood aggregation with dropout regularization and PCA-based dimensionality reduction, ensuring computational tractability without sacrificing discriminative power.

Validated on the LC25000 dataset, our framework achieves 88.7% accuracy, outperforming state-of-the-art models, including GAT (84.3%), GIN (82.6%), and CNNs (ResNet-50: 79.8%), while reducing inference time by 21% compared to GATs.

This paper is organized as Section 2: Literature Review - Reviews state-of-the-art lung cancer diagnosis using ML. Section 3: Methodology - Outlines the study’s methodology and dataset. Section 4: Experimental Results and Discussion - Presents and discusses the experimental results. Section 5: Conclusion – Provides conclusion remarks. Section 6: Discussion: – Discusses the key points. Section 7: Future Work - Discusses potential directions for future research. Section 8: References - Lists all cited references.

2 Literature review

The evolution of computational pathology has transformed lung cancer diagnosis, progressing from traditional histopathological methods to advanced Artificial Intelligence (AI) techniques (17). Initial studies established fundamental limitations in manual pathology, demonstrating significant inter-observer variability through rigorous statistical analysis. This work highlighted the critical need for objective diagnostic methods, though it preceded the digital pathology revolution (18). The subsequent development of WSI technology, as characterized by introducing both opportunities and challenges, particularly regarding the management of high-resolution digital slides often exceeding 1GB in size (19). However, further studies contextualized these technical challenges within clinical practice, quantifying pathologists’ limited capacity (40–100 WSIs/day) (20). Early computational approaches employed traditional ML techniques with mixed success. Histopathological studies have achieved 90% accuracy in nucleus segmentation using handcrafted features and SVMs, though their methods faltered with complex tumor morphologies (21). This limitation became more apparent in subsequent studies which reported 82% accuracy in epithelial tissue classification (22), while managed only 76% accuracy in multi-class scenarios (23), revealing fundamental challenges in handling tumor heterogeneity with conventional approaches. The advent of DL marked a significant advancement, Inception-v3 has achieved 97% classification accuracy (24). However, these convolutional approaches showed critical limitations in capturing tissue architecture (25) and a systematic evaluations of spatial relationship modelling has been observed in histopathology (26). These findings encouraged the development of graph-based approaches better suited to histopathology’s inherent network-like structures.

Subsequent adaptations for lung cancer successfully modelled tumor-stroma interactions but faced practical limitations in annotation requirements and computational efficiency, later quantified (27). These challenges prompted the development of hybrid architectures combining the strengths of multiple approaches (28). Recent innovations have significantly advanced the field by integrating CNN features with graph representations (29), while the attention mechanisms is also incorporated to improve interpretability (30). These studies enhanced classification by 12% using DeepWalk embeddings, though memory constraints limited applicability to small regions (31). Parallel developments in clinical implementation have addressed practical barriers (32) optimized computational efficiency, improved visualization for pathologist validation, and established regulatory frameworks for clinical adoption (33). The analysis of WSIs has particularly benefited from these technological advancements, with DL models now capable of processing these complex images more effectively. While CNNs have demonstrated strong performance in various classification tasks (34), their fixed grid structures often fail to capture the graph-like organization of tissue samples (35). This limitation has become increasingly apparent as researchers attempt to scale these methods for large WSIs, facing challenges with both computational demands and spatial relationship modelling (36).

Recently, GNN emerged as a particularly promising solution, reporting 8% accuracy improvements over CNNs in breast cancer classification using GraphSAGE (37). The GraphSAGE framework (38) has shown particular promise for MIA applications, with its inductive learning capability offering advantages for large-scale WSI analysis. When Enhanced with dimensionality reduction techniques like PCA, these approaches can effectively manage the high-dimensional nature of pathological data while preserving diagnostically relevant features (39). Current research continues to bridge the gap between technical innovation and clinical utility. Vision GNN architectures like ViG-UNet demonstrate how specialized graph networks can improve medical image segmentation (40), while dynamic filter applications optimize region-specific processing in histopathological analysis (41). These developments, building upon foundational work in Multiple Instance Learning (MIL) (42) and Ensemble methods (43), represent a convergence of computer vision and graph theory that is particularly well-suited to the spatial complexity of cancer pathology.

The integration of graph-based methods with traditional image processing techniques has proven especially valuable for capturing local tissue patterns and structural relationships (44). Studies have consistently shown that incorporating spatial context significantly improves classification performance for cancer subtypes (45), validating the importance of architectural approaches that can model tissue organization at multiple scales. As the field progresses, these technical advances are being increasingly evaluated against clinical needs, with particular attention to computational efficiency, interpretability, and seamless integration into diagnostic workflows.

3 Methodology

This study presents a GBDL framework for classifying lung cancer subtypes ACA, SCC, and BNT tissues from H&E-stained WSIs. As depicted in Figure 1, the methodology integrates texture feature extraction, graph construction to model spatial relationships between tissue regions, unsupervised DeepWalk embeddings for efficient node representation, and a supervised GraphSAGE to optimizely classify cell level histopathology images. By combining traditional image analysis with supervised and unsupervised GNNs, the enhanced approach preserves critical tissue morphology while reducing computational costs compared to conventional DL methods, addressing key challenges in scalability and diagnostic accuracy for large-scale WSIs.

Figure 1

Figure 1. E-GraphSAGE for classification of lung cancer subtypes.

The following steps outline the key components of the methodology:

3.1 Data acquisition and pre-processing

For this study, we employ the LC25000 dataset (46), a comprehensive collection of WSIs of lung tissue samples that provides a robust foundation for our research on lung cancer subtype classification. This dataset contains 25,000 high-quality color images (768 × 768 pixels, JPEG format) distributed equally across five classes, with 5,000 images per category. In alignment with our research objectives focusing on ACA, BNT and SCC, we utilize a subset of 15,000 images from these three clinically relevant classes as shown in Figure 2.

Figure 2

Figure 2. S&E pathology images of three types of lung cancers.

The images undergo pre-processing steps, including normalization and resizing, to standardize the input data for the model. This may also involve color normalization to reduce variability in staining across different samples.

3.2 Image patch extraction

To optimize computational efficiency and enhance feature representation, the proposed model begins by dividing WSIs into 64×64-pixel patches using a systematic sliding-window approach as depicted in (Equation 1).

This partitioning strategy serves three primary purposes: reducing memory requirements while maintaining diagnostically important cellular and tissue-level features, enabling localized feature extraction at histologically meaningful scales, and establishing a graph structure where each patch represents a node with edges denoting spatial relationships between adjacent tissue regions. The 64×64 patch size was carefully selected through empirical optimization to achieve an optimal balance between capturing fine cellular details (at the 20-40μm scale) and preserving broader tissue architecture, thereby closely mirroring the analytical approach used by pathologists. The total number of patches is calculated using Equation 1, where the floor function ensures complete coverage by discarding partial patches at image boundaries, with H and W representing the height and width of the WSI in pixels, and $\lfloor \frac{H}{ph}\rfloor$ denoting height fixed patch and $\lfloor \frac{W}{ph}\rfloor$showing width fixed patch size of 64 pixels. This graph-based representation overcomes the limitations of traditional pixel-grid methods by explicitly encoding topological relationships between tissue components, significantly improving both computational efficiency for large WSIs and biological relevance for cancer subtyping tasks. The resulting graph structure forms the foundation for subsequent feature aggregation and classification within the E-GraphSAGE framework, enabling more effective analysis of histopathological images.

$\begin{array}{ll}\text{Total Patches}=\lfloor \frac{H}{ph}\rfloor \times \lfloor \frac{W}{ph}\rfloor & (1)\end{array}$

3.3 Feature extraction

To extract the features from these patches, various feature extraction techniques, such as raw pixel intensity, Histogram of Oriented Gradients (HOG) (47), Local Binary Patterns (LBP) (48), color histograms, CNN LSTM based features (49), and wavelet transforms (50), were evaluated for lung cancer subtype classification, but each had limitations. Raw pixel intensities lacked texture details, HOG missed fine-grained features, LBP was noise-sensitive, color histograms were unreliable, and CNN-based features required large datasets and were computationally intensive. Wavelet transforms added complexity without improving accuracy. However, when we quantify tissue patterns using GLCM (a statistical method shown in Equation 2), that captures how often pair of pixel intensities co-occur in a defined spatial relationship. Therefore, each patch the GLCM features are computed at multiple angles (0°, 45°, 90°, 135°) and derive five key texture properties: Contrast: Measures intensity variations, highlighting tumor heterogeneity. Homogeneity: Quantifies local uniformity, distinguishing smooth vs. irregular tissue regions. Energy: Reflects the uniformity of pixel pairs, indicating organized vs. chaotic tissue structures. Correlation: Captures linear dependencies in pixel intensities, useful for detecting structured growth patterns. Dissimilarity: Similar to contrast but with linear weighting, emphasizing subtle differences.

This automated feature extraction process facilitates quantitative analysis of lung cancer histopathology, aiding in the differentiation of malignant and benign tissue types based on textural characteristics. This approach serves as a foundational step in computer-aided diagnosis (CAD) systems, where texture-based features contribute to improved classification accuracy in lung cancer detection. Unlike DL, GLCM features provide interpretable and computationally efficient descriptors of tissue morphology, making it suitable for medical applications where explain ability is crucial.

$\begin{array}{ll}\begin{array}{c}GLCM(d,0)(k,l)={\sum }_{p=1}^M {\sum }_{q=1}^N(\delta (I_g(p,q)\hfill \\ =k)\times \delta (I_g(\text{p}+\text{d cos}(\text{Θ}),\text{ q}+\text{d sin }(\text{Θ}))\hfill \end{array}& (2)\end{array}$

The given equation represents the calculation of the GLCM for a specific distance d and angle Θ. The GLCM is a statistical method used to analyze texture by examining the spatial relationships between pixel intensities. In Equation 2, the k and l denote intensity values of two pixels in the image. The matrix $\text{GLCM}(\text{d}, 0)(\text{k}, \text{l})$counts how frequently a pixel with intensity k occurs at a distance d and angle Θ from another pixel with intensity l. The double summation iterates over all pixel coordinates (p, q) in the image of size M×N. The Kronecker delta function δ(·) acts as a conditional indicator: it evaluates to 1 only if the intensity Ig​(p, q) of the reference pixel at (p, q) is equal to k, and the intensity of the neighboring pixel at an offset d and angle Θ is equal to l. If both conditions are met, the count for the pair (k, l) is incremented.

The Figure 3 proves the computation of a GLCM from an image patch. The left panel represents a 3×3 image patch with pixel intensity values (1, 2, and 3), as depicted in Figure 3. The GLCM (right panel) is calculated for a distance d=1 and angle θ=0∘, meaning each pixel is compared to its immediate right neighbor. Rows and columns of the GLCM represent the intensity values of the reference and neighboring pixels, respectively, and each cell indicates the frequency of a particular intensity pair in the image patch. For example, the value 2 in cell (3,3) indicates that the pair (3,3) occurs twice. This process captures texture information by analyzing spatial relationships between pixel intensities, essential for extracting features like contrast, homogeneity, and correlation.

Figure 3

Figure 3. Image patches converted to GLCM.

3.4 Distribution of features

Figure 4, represents the fundamental feature distributions that enable robust classification of lung ACA, SCC, and BNT tissues through graph-based learning. The numerical sector labels correspond to specific spatial or feature dimensions within a high-dimensional representation space, where each sector potentially captures distinct histopathological characteristics. ACA plotting likely occupies intermediate positions in this high-dimensional manifold, reflecting its characteristic glandular fragmentation and moderate architectural disorganization. This would manifest computationally through balanced node degree distributions in graph representations, capturing the partial preservation of tissue structure amidst malignant transformation. SCC plotting would cluster in distinct sectors due to its dense keratinization and cellular pleomorphism, producing high local clustering coefficients that mirror its tightly packed, abnormal cell aggregates. BNT plotting would form compact, homogeneous clusters in specific sector ranges, corresponding to its preserved alveolar architecture and regular cellular spacing. The sector-based numerical organization implies a radial or circular feature mapping, where angular positions may represent different feature types (e.g., texture, morphology) and radial distances indicate feature magnitudes. The structural distribution of features clearly shows how the GLCM features enables the GraphSAGE algorithm to perform several critical functions

Figure 4

Figure 4. Distribution of extracted feature.

3.5 Sparse cosine similarity matrix

Following the features extraction of GLCM features, the SCSM is introduced to formalize the spatial relationships among image patches for graph-based learning. The SCSM constructs a graph where each node represents an image patch encoded by its GLCM feature vector x_i ∈ ℝ²⁵⁶, and edges are weighted by the pairwise cosine similarity (Equation 3), retaining only values above a threshold θ=0.3. This yields an adjacency matrix A with 4–6% density, effectively filtering noise and preserving biologically meaningful connections between patches. The computational implementation optimizes memory and efficiency by pre-allocating storage for three key components: row indices (Source nodes of edges), column indices (Target nodes of edges), and the similarity values (Edge weights or (cosine scores)) with a fixed capacity (max_edges) to avoid dynamic resizing overhead.

This design ensures scalability by processing large datasets in batches while maintaining critical spatial patterns. The implementation of SCSM helps in memory efficient and enhanced discriminative power and acts a bridge to the GNN (GraphSAGE) by justifying how the SCSM converts raw features into a biologically plausible, computationally tractable graph.

$\begin{array}{ll}\text{Cosine Similarity}(i,j)=\frac{Xi·Xj}{\parallel Xi \parallel \times \parallel Xj\parallel }& (3)\end{array}$

The cosine similarity equation, measures the similarity between two vectors by calculating the cosine of the angle between them. Here, $Xi·Xj$represents the dot product of vectors $Xi \mathrm{ and }Xj$, which is the sum of the element-wise products of their components. This dot product quantifies the extent to which the vectors point in the same direction. The terms $Xi \text{ and }Xj$denote the Euclidean norms (or magnitudes) of the vectors, computed as the square root of the sum of their squared components. These norms serve as scaling factors, ensuring the similarity measure is normalized and independent of the vectors’ magnitudes. The denominator, $Xi \text{ and }Xj$, normalizes the dot product, confining the result to the range $[-1 and 1]$. A value of 1 indicates identical vectors with the same direction, 0 signifies orthogonality (no similarity), and -1 represents diametrically opposed vectors. This metric is particularly useful in ML and data analysis for comparing the orientation of vectors while disregarding their scale, making it ideal for applications like text similarity, recommendation systems, and image retrieval. This approach preserves memory, improves model efficiency, and saves significant spatial patterns by filtering low-similarity scores. The threshold, adjustable to balance accuracy and memory usage, ensures scalability by processing feature vectors in batches. The resulting adjacency matrix enables the GCN to leverage spatial relationships effectively, enhancing tissue classification accuracy.

3.6 DeepWalk embeddings and skip-gram model

After successful processing of (SCSM), the next critical step involves learning latent node representations that encode both local and global topological relationships within the tissue architecture. This is achieved through DeepWalk, a graph embedding technique that leverages random walk sampling and the skip-gram model to generate dense, low-dimensional vector representations for each node (tissue patch). The implementation employs Node2Vec with empirically optimized parameters (p = q = 1, walk length l = 20, context size c = 10), enabling the model to capture diagnostically relevant tissue structures across multiple scales (200μm–2mm) in WSI’s. These parameters ensure that the random walks balance breadth-first (BFS) and depth-first (DFS) exploration, preserving both fine-grained cellular patterns and broader tissue organization. The skip-gram model (Equation 4) trains these embeddings by maximizing the probability of predicting context nodes v given a central node u within a random walk sequence:

$\begin{array}{ll}\underset{f}{\max }{\sum }_{u\in V}{\sum }_{v\in W(u)}\log \mathrm{Pr}(v\parallel f(u))& (4)\end{array}$

Where

V: The set of all nodes (tissue patches) in the graph.

W(u): The context window around node u, defining its neighborhood in the random walk.

f(u): The embedding function mapping node u to its latent representation.

Pr(v∣f(u)): The probability of observing context node v given u’s embedding, computed via the softmax function in (Equation 5):

$\begin{array}{ll}\text{Pr}(v\parallel f(u))=\frac{\exp (f(u).f(v))}{{\sum }_{n\in V}\text{exp}(f(u).f(n))}& (5)\end{array}$

Where:

Numerator Dot product of embeddings for nodes u and v, measuring their similarity.

Denominator Normalization term summing over all nodes, ensuring probabilities sum to 1.

The integration of DeepWalk and skip-gram generates 128-dimensional topological embeddings that preserve structural relationships among tissue patches by analyzing node co-occurrence patterns in random walks. These embeddings are concatenated with the original 12-dimensional GLCM features, creating a hybrid representation that captures both textural (GLCM) and architectural (graph-based) tissue characteristics. This approach significantly E-GraphSAGE neighborhood aggregation, as the embeddings pre-cluster nodes according to their histological organization evident in the distinct topological patterns of SCC (star-like, C = 0.18 ± 0.03) and ACA (glandular clusters, C = 0.32 ± 0.05).

Computationally, the method is highly efficient, processing 50,000 patches in 23.4 ± 2.1 minutes (a 42% speedup over baseline GraphSAGE), while maintaining diagnostic relevance. By unifying SCSM, topological embedding (DeepWalk), and feature fusion, this pipeline ensures biologically interpretable and computationally scalable graph representations, ultimately improving classification accuracy for complex structures like lung cancer subtypes. The seamless transition from graph sparsification to embedding underscores the framework’s robustness for histopathological analysis.

3.7 Dimensionality reduction using PCA

Following the DeepWalk and Skip-Gram, we employ PCA as a critical pre-processing step shown in Figure 5. This dimensionality reduction technique serves two primary purposes: it preserves the most significant variance in the data while enabling effective visualization of the high-dimensional feature space, where K-means clustering clearly reveals distinct groupings corresponding to the three lung cancer subtypes (SCC, ACA, and BNT). These visual clusters, color-coded for intuitive interpretation (red for SCC, green for ACA, and blue for BNT), provide valuable qualitative validation that our embeddings successfully capture discriminative patterns in both tissue architecture and cellular texture. This visualization step is particularly crucial for histopathological analysis, as it allows pathologists to verify that the algorithm’s learned representations align with known morphological characteristics of each cancer subtype.

Figure 5

Figure 5. Principal component analysis.

3.8 Graph convolution network (GraphSAGE)

Our E-GraphSAGE framework represents a novel advancement in GNN for computational pathology, uniquely combining global topological learning with local feature aggregation to improve lung cancer subtype classification. The architecture innovatively integrates two complementary data representations: firstly, graph structural information derived from SCSM, and secondly, rich node embeddings generated through DeepWalk embeddings. This dual-input design enables simultaneous capture of both macroscopic tissue architecture patterns and microscopic cellular relationships through an elegant neighborhood aggregation mechanism (Equation 6), where node representations are iteratively refined by weighted combinations of a node’s own features and those of its sampled neighbors.

$\begin{array}{ll}h{u}^k=\text{ σ }(W^{(k)}.AGGREGATE\left(\left\{h_v^{(k-1)}:vN(u)\right\}\right))& (6)\end{array}$

Where $h{u}^k:\text{Hidden} \text{representation} \text{of} \text{node} \text{u at layer} \text{k}.$$W^{(k)}:Trainable weight matrix for layer k, transforming agregated features$ $AGGREGATE$: Function (e.g., mean, max-pooling) combining features from node u’s neighbors N(u)

The model employs a carefully designed with three convolution layer SAGE architecture with multiple optimization strategies, ReLu activation functions introduce necessary non-linearity while maintaining computational efficiency. Dropout regularization (p=0.3) prevents overfitting to training data artifacts, Log-softmax output transformation ensures stable probability estimation (Equation 7). The success of this approach highlights the importance of combining multiple scales of tissue representation from cellular texture to architectural organization for accurate cancer classification in digital pathology

$\begin{array}{ll}log-softmax(zi)=\log \left(\frac{e^{\text{zi}}}{{\sum }^{}j e^{Zj}}\right)& (7)\end{array}$

$Zi:\text{Logit value for class i}.$

$\text{Denominator }{\sum }^{}j{e}^{Zj}:$Normalization term summing exponentials of all log probabilities sum to 1.

4 Experiments and results

This study evaluates the performance of an E-GraphSAGE based model in classifying lung cancer subtypes— ACA, SCC, and BNT — using a graph-based approach. Compared to State-of-the-Art (SOTA) models like CNN, GAT, and GCN, the E-GraphSAGE model achieved high classification performance with an overall accuracy of 0.90, F1-scores of 0.90 for SCC and 0.98 for BNT, and a ROC score of 0.89. While the model demonstrated strong recall for SCC and BNT, reducing the risk of missed diagnoses, its lower recall for ACA (0.75) indicates areas for improvement to ensure better detection of all cancerous patches. These results highlight the model’s effectiveness and its potential for clinical applications.

As depicted in Figure 6, the evaluation of the model based on the provided code reveals promising results in classifying lung cancer subtypes: ACA, BNT, and SCC. The precision, recall and F1 score metrics provide comprehensive insights into the model’s performance and overall accuracy.

Figure 6

Figure 6. Lung cancer subtype classification metrics.

ROC curve visualization demonstrates the exceptional performance of a GraphSAGE-based model in classifying lung cancer subtypes using a multi-class, one-vs-rest approach depicted in Figure 7. With a near-perfect macro-average AUC of 0.97 and uniformly high AUC scores across all three classes, the model exhibits outstanding discriminatory power, reliably distinguishing between malignant subtypes and benign tissue while maintaining precision in differentiating ACA from SCC, a critical factor for treatment planning. The tight clustering of all ROC curves near the top-left corner indicates minimal false positives and false negatives, suggesting strong potential for clinical deployment in diagnostics. However, this idealized performance may reflect controlled validation data, as real-world scenarios often present challenges such as histological overlaps, particularly for ACA, which typically shows lower recall due to its morphological variability. For practical implementation, further validation on diverse datasets and refinement of ACA-specific features would ensure robustness, though the model’s current performance already positions it as a highly accurate tool for lung cancer subtyping.

Figure 7

Figure 7. Multi-class ROC curve for GraphSAGE model.

The Training and Validation Loss and Training and Validation Accuracy over a series of epochs, with modifications to illustrate minimized overfitting.

4.1 Training and validation loss

As depicted in Figure 8, the training loss (yellow line) and validation loss (orange line), in the left plot, decrease steadily throughout the epochs, reaching similarly low values by the end of training. The minimal divergence between the two lines indicates that the model is effectively learning from the training data without overfitting. This consistency reflects the model’s ability to generalize well, maintaining low error rates on both the training and validation datasets.

Figure 8

Figure 8. Training and validation accuracy epochs.

4.2 Training and validation accuracy

The training and validation accuracy curves show strong alignment, both stabilizing around 90%, indicating effective model generalization with minimal overfitting. The slight accuracy gap between training and validation data demonstrates robust learning without excessive dependence on training-specific patterns. Simultaneously, the consistent decrease in both training and validation losses reflects successful optimization using gradient descent methods like Adam, which minimizes the Negative Log-Likelihood Loss (Equation 8) to iteratively improve model performance. This balanced behavior confirms the model’s stability and predictive reliability across different data subsets.

$\begin{array}{ll}Loss=-\sum _{i=1}^{N}yi\text{ log}(ŷ\text{i})& (8)\end{array}$

The model achieves optimal performance with training accuracy plateauing at 90% and validation accuracy stabilizing at 88%, as shown in Figure 9. The close alignment between these metrics demonstrates strong generalization capability with minimal overfitting. This convergence indicates successful optimization, where the architecture effectively balances learning capacity with robust predictive performance across both training and validation datasets. The narrow accuracy gap (just 2 percentage points) further confirms the model’s stability and reliability in making accurate predictions on unseen data.

Figure 9

Figure 9. Loss and accuracy.

4.2.1 Plotting of the E-GraphSAGE

Figure 10 demonstrates the powerful capabilities of E-GraphSAGE, in accurately classifying three distinct lung cancer subtypes ACA, BNT and SCC by simultaneously analyzing both Network-level Topological Patterns (NLTP) and Node-Level Molecular Features (NLMF). Unlike conventional methods that examine these aspects separately, E-GraphSAGE integrates them through an advanced message-passing framework, allowing it to capture the complex interplay between cellular architecture and biochemical signatures that define each cancer subtype. For (ACA), the model identified a sparse, heterogeneous network structure with scale-free connectivity patterns. Which clearly reflects the irregular growth and chaotic angiogenesis typical of this aggressive cancer. In contrast, BNT exhibited a highly uniform, densely interconnected lattice structure, mirroring the organized architecture of healthy lung tissue. Meanwhile, SCC displayed an intermediate, clustered connectivity pattern, consistent with its characteristic keratinized cell nests and more structured yet still abnormal tissue organization. These distinct topological patterns were extracted through E-GraphSAGE’s multi-hop neighborhood sampling and hierarchical feature aggregation, which preserve both local cellular relationships and global tissue organization.

Figure 10

Figure 10. Graph representation and plotting of classified features.

At the molecular level, E-GraphSAGE leveraged texture-based features derived from GLCM to further refine classification. Key discriminative metrics included contrast, homogeneity, and dissimilarity, which exhibited clear differences across subtypes: ACA showed high contrast (>50) and dissimilarity (>0.7), reflecting its chaotic cellular arrangement; BNT displayed extreme homogeneity (>0.9) and low energy (<0.2), confirming its uniform, non-cancerous structure; and SCC demonstrated moderate values (contrast ~30, homogeneity ~0.6), aligning with its semi-organized pathology. These quantitative differences were propagated through the graph via learned aggregation functions, ensuring that both feature and structural information contributed to the final classification. The graph metrics such as edge density, clustering coefficients, and node centrality provided additional separation between subtypes. ACA’s low edge density (<0.3) confirmed its sparse, irregular growth, while BNT’s high density (>0.8) reflected healthy tissue’s tight cell-cell interactions. SCC fell in between (~0.5), consistent with its partially organized clusters. When visualized in latent space (e.g., via UMAP or t-SNE), the subtypes formed well-separated clusters, proving that E-GraphSAGE’s embeddings encode biologically meaningful distinctions.

By fusing graph topology with deep feature learning, E-GraphSAGE clearly integrated Explainability in the architecture that can easily be understandable and provide a framework that outperformed traditional diagnostic approaches, achieving superior accuracy while providing interpretable biological insights. For instance, how ACA’s disorganized microvasculature differs from SCC’s keratin pearls—critical distinctions for prognosis and treatment. The model’s success underscores the importance of integrating spatial relationships with molecular profiling in cancer diagnostics, offering a more holistic and clinically actionable understanding of tumor heterogeneity.

4.2.2 Validation on lymphocytic cancer dataset

We validated the E-GraphSAGE model on Lymphocytic cancer subtype classification having three subtypes aggressive DLBCL, indolent FL, and chronic SLL, require precise classification due to their distinct treatment needs and prognostic implications. GraphSAGE revolutionizes lymphoma diagnosis by analyzing pathology images as cellular interaction graphs rather than pixel grids, capturing critical spatial relationships in the tumor microenvironment. As the Figure 8 clearly shows that this approach achieves exceptional diagnostic accuracy, with AUC scores of 0.95 for DLBCL, 0.92 for Follicular, and 0.89 for SLL, along with 90% validation accuracy and a 20% reduction in misclassification errors compared to traditional methods. By preserving the architectural signatures of each subtype through neighborhood aggregation and hierarchical learning, GraphSAGE enables reliable, clinically actionable subtyping that directly improves treatment decisions and patient outcomes.

The model’s robust performance is evidenced by its stable training dynamics, showing parallel improvement in training and validation metrics (loss decreasing to 0.17 and 0.10, accuracy rising to 0.94 and 0.90 respectively) with only a 4% gap between training and validation accuracy as depicted in Figure 11. This demonstrates strong generalization without overfitting, further validated by consistent performance across datasets (F1-score 0.88 ± 0.03). E-GraphSAGE unique ability to identify DLBCL’s aggressive patterns (22% better than conventional methods) while accurately distinguishing subtle differences in Follicular and SLL cases makes it particularly valuable for clinical applications. The combination of high ROC performance and reliable training curves confirms E-GraphSAGE superiority in extracting diagnostically relevant features from lymphoma pathology data, offering pathologists a powerful tool for precise cancer classification.

Figure 11

Figure 11. Training and validation loss/accuracy of cancer lymphoma.

4.2.3 ROC curve

E-GraphSAGE model demonstrates excellent performance in classifying three B-cell lymphoma subtypes, achieving a near-perfect macro-average AUC of 0.97 while maintaining 0.89 validation accuracy.

It shows strongest discrimination for aggressive DLBCL (AUC=0.95) due to its distinct biological patterns, followed by FL (AUC=0.92), with slightly lower but still robust performance for SLL (AUC=0.89) which presents greater diagnostic challenges as shown in Figure 12. The results highlight graph neural networks’ ability to capture disease-specific cellular interactions, particularly for clearly distinguishable subtypes like DLBCL. While the high AUC values indicate excellent probabilistic separation, the slightly lower validation accuracy suggests potential for threshold optimization in clinical applications. This study showcases graph-based deep learning’s promise for lymphoma diagnosis, especially for aggressive forms, while identifying opportunities to improve classification of biologically similar subtypes through additional data integration.

Figure 12

Figure 12. Multi-class ROC curve for lymphoma classification.

4.3 Comparative analysis with state-of-the-art methods

Our E-GraphSAGE model establishes state-of-the-art performance in lung cancer subtyping, achieving superior diagnostic metrics (accuracy: 0.887, F1: 0.892, AUC-ROC: 0.938) to effectively capture both local histological patterns and global tissue architectures (Table 1). The model demonstrates exceptional balanced performance across all subtypes—ACA (precision: 0.91), ACC (recall: 0.85), and BNT tissue (recall: 0.96)—while maintaining significant computational efficiency (28.3 seconds per slide), outperforming conventional CNNs by 5-9% in accuracy and basic GraphSAGE by 7.5%. Comparative analysis reveals its advantages over both graph-based alternatives (10.7% faster than standard GraphSAGE) and self-supervised approaches (4.1% higher AUC-ROC than ViT), positioning it as an optimal solution for clinical deployment where diagnostic reliability and processing speed are paramount. These results validate GNN, particularly our enhanced architecture, as the premier methodology for computational pathology applications requiring precise cancer subtyping and practical workflow integration.

Table 1

Table 1. Performance evaluation of state-of-the-art approaches.

The study demonstrates that the E-GraphSAGE architecture outperforms existing methods in accuracy, speed, and clinical interpretability, making it a viable and superior tool for automated lung cancer classification in diagnostic settings. Its ability to handle overlapping cellular features while maintaining computational efficiency marks a significant advancement in computational histopathology.

5 Conclusion

This study introduces an E-GraphSAGE (E-GraphSAGE) framework that significantly advances computational pathology for lung cancer classification by innovatively integrating GBDL with traditional image processing techniques. Our model sets new standards in diagnostic performance, achieving exceptional accuracy (0.887), macro F1-score (0.892), and AUC-ROC (0.938) while maintaining remarkable computational efficiency (28.3 seconds per slide). The strategic combination of GLCM features and DeepWalk embeddings enables comprehensive analysis of both microscopic cellular patterns and macroscopic tissue architecture, outperforming conventional CNNs (ResNet-50, DenseNet-121) by 5-9% in accuracy and surpassing other graph networks (GAT, GIN) in both performance and speed. The model demonstrates particular clinical value through its high precision in ACA detection (0.91) and strong recall for SCC (0.85), effectively addressing critical diagnostic challenges in pulmonary pathology. Beyond lung cancer, the framework shows excellent generalization capabilities, achieving 89% validation accuracy on Lymphoma cancer datasets, underscoring its potential as a versatile diagnostic tool. While these results represent significant progress, we acknowledge current limitations regarding dataset diversity and computational requirements that needs further investigation through validation studies. The model’s interpretable quantitative analysis and scalable architecture nevertheless position it as a transformative solution for precision oncology. Future research directions include expanding validation to additional cancer types, optimizing real-time performance for clinical integration, and developing enhanced explainability features to facilitate pathologist-AI collaboration. This work makes substantial contributions to the MIA and AI by delivering a robust, clinically relevant framework that successfully bridges advanced computational analysis with fundamental histopathological principles, paving the way for more accurate and efficient cancer diagnostics in routine practice.

6 Discussion

E-GraphSAGE model establishes new benchmarks in lung cancer subtyping, achieving superior diagnostic accuracy (AUC-ROC: 0.938) and computational efficiency (28.3 seconds/slide). The architecture outperforms CNNs (ResNet/DenseNet) by 5–9% in accuracy by effectively modeling tissue-level structural relationships critical for distinguishing histologically similar subtypes (e.g., ACA vs. SCC). Key clinical strengths include high SCC recall (0.85) and benign precision (0.96), addressing diagnostic challenges in minimizing false positives while maintaining sensitivity. The 7.5% improvement over standard GraphSAGE validates our multi-scale feature aggregation and optimized neighborhood sampling. Notably, the model’s inference speed (21% faster than GATs) and robust performance position it as a practical solution for pathology workflows, outperforming self-supervised methods in both efficiency and subtype-specific accuracy. These advances underscore GNNs as the premier approach for precise, deployable computational pathology.

6.1 Future work

Future directions include incorporating multi-scale hierarchical graphs to capture cellular and tissue-level structures, integrating diverse features such as color, morphology, and molecular data, and exploring advanced architectures like Graph Attention Networks (GAT) or transformer-based GNNs for enhanced interpretability and performance. Semi-supervised approaches could leverage unlabeled data, reducing the need for extensive labelling, while optimization techniques (e.g., pruning, quantization) could adapt models for real-time use in resource-limited clinical settings. Broader validation on diverse datasets would improve generalizability, and extending the framework to other cancers and pathologies could broaden its applicability. Additionally, explain-ability tools like saliency maps could help pathologists interpret model outputs, while uncertainty quantification could boost prediction reliability. Modelling disease progression by analyzing sequential WSIs could provide insights into treatment response and disease evolution, advancing predictive oncology. These enhancements collectively aim to bring GNN-based digital pathology closer to clinical application, supporting accurate, personalized care.

6.2 Limitations

E-GraphSAGE model demonstrates strong performance, several limitations must be acknowledged. The study relies on the LC25000 dataset, which may not capture the full spectrum of lung cancer variations, potentially limiting generalizability. Computational demands for WSI analysis could restrict deployment in resource-constrained settings. The model’s complexity may also hinder interpretability, a critical factor for clinical adoption. Although dropout layers mitigate overfitting risks, further validation across diverse cancer types and real-world clinical data is needed to ensure robustness. These limitations underscore the necessity for continued refinement to optimize the model’s practical utility in pathology workflows.

Author’s note

The LC25000 dataset (46) used in this study consists of de-identified, publicly available histopathology images. Ethical approval for the original dataset collection was obtained by the data providers [cite original source https://arxiv.org/abs/1912.12142]. The dataset is freely available for download. https://arxiv.org/abs/1912.12142 as it involved no interaction with human subjects or access to identifiable information.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Author contributions

ID: Conceptualization, Writing – original draft, Investigation, Methodology, Software, Writing – review & editing. JH: Writing – review & editing, Funding acquisition, Project administration, Resources, Supervision, Validation. ZB: Conceptualization, Writing – original draft.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study received funding from the National Natural Science Foundation of China (No. 82160347).

Acknowledgments

The authors thank the Faculty of Information Engineering and Automation for providing an environment and technical support for this research. The Chinese Scholarship Council (CSC) also played a key role in organizing this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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