Bridging the STEM gender gap through cultural inclusion and educational opportunity, this opportunity was granted to a selected set of women from UB to showcase their research.

1. • Crop whole-slide image to focus on individual satellites and tumor interface
• Two approaches:
1. Obtaining surface vertex points in 3D (MATLAB): Clustering groups the
detected surface points as either tumor or satellite
2. STL model creation (MeshLab): Vertex points imported into MeshLab for
visualization – qualitative assessment of reconstruction fidelity [3]
Background of Oral Cavity Cancer 3D Modeling
Data Collection and Tumor Classification
Tumor-Host Interface Visualization and Feature Extraction: Application to Oral Cavity Cancer
Kritika Lakhotia1, Stephen Schneider1, Margaret Brandwein-Gensler2, Scott Doyle1
1 Department of Biomedical Engineering, 2Department of Pathology and Anatomical Sciences
Introduction
Dataset Description and Slice Registration
• Tissue resections harvested from three patients with low-stage (I/II) OCC
• Serial sections obtained at 6 µm thickness and stained with Hematoxylin and Eosin
• Slide digitization at 40x optical magnification and images resized to 32 µm per pixel
• Include entire tumor mesh along with their satellites (for correct classification)
• Precise segmentation at higher-resolution for validation, prevent inaccurate
delineation of tumor region
• Advanced classification and segmentation techniques – patch-based approach to
curb noise associated with initial tumor boundaries
2D Histology to 3D Models
Classification of Tumor Region
Original RGB Image Gray-level Texture Filter Classifier Prediction
Feature Robustness to Mesh Decimation
• Framework for illustration of 3D OCC models: Adds significant diagnostic
information to currently used 2D tissue sections & easy visualization of tumor
• Pre-constructed mesh for pathologists: Fast prediction of tumor recurrence
• Promising system for quantitative approach for predicting locoregional recurrence
• System scope for non-obvious features like surface curvature, normal vector analysis,
interactions between structure and function
• Work to increase patient dataset for significant statistical power
Discussion
• Correlates to pathological risk model: WPOI-5 type carcinomas defined by minimum 1
mm distance between tumor masses [2]
• Min. distance value for Case 1: Low tumor risk (WPOI-3), Case 2: High tumor risk
(WPOI-5), Case 3: Contradiction, would be categorized as low risk of recurrence
• Oral Cavity Cancer (OCC): 48,000 cases, 9,000 deaths in 2016 (ACS Statistics)
• High-stage OCC (Stage III/IV) – Receive aggressive multimodal therapy
• Low-stage OCC (Stage I/II) – Receive only hormone therapy
• Some low-stage patients develop locoregional recurrence
• Quantitative Risk Score model developed - predicts recurrence in low-stage patients
according to Worst Pattern of Invasion (WPOI) [1]
• 3D architecture yields additional criteria for predicting recurrence
• Tumor satellite volume & spatial extent quantitatively measured from 3D model
Model Feature Extraction
• Performed on reconstructed 3D meshes
• Features of interest: Satellite volume, minimum distance from main tumor body
• Larger minimum distances & larger volumes – Likeliness of an aggressive tumor
• Mesh decimation reduces computational complexity and memory requirements
• Meshes were decimated up to ten times by uniform removal of vertex points
• Visually, same structure for a non-decimated (left) and a decimated mesh (center)
• Minimum distances (right) between 22.5 to 27.5 pixels and total variation in feature
value = 0.004% due to decimation (negligible difference)
Non-decimated Mesh Decimated Mesh
Minimum Distance vs.
Decimation Severity
References
1. Margaret Brandwein-Gensler et al. Oral squamous cell carcinoma: histologic risk
assessment, but not margin status, is strongly predictive of local disease-free and
overall survival. Am. J. of Surg. Path., 29(2):167{178, 2005.
2. Margaret Brandwein-Gensler et al. Validation of the histologic risk model in a new
cohort of patients with head and neck squamous cell carcinoma. Am. J. of Surg.
Path., 34(5):676{688, 2010.
3. Visual Computing Lab ISTI CNR. Meshlab. http://meshlab.sourceforge.net/.
Extracted Features and Pathological Grade
Future Work
Low-risk OCC Tumor High-risk OCC Tumor
• Leftmost column: WPOI - 3, middle and right columns: WPOI – 5
• Curvature models – Green: low curvature, Red: high convexity, Blue: high concavity
Original Images
3D Models (Curvature)
Mesh Reconstruction
Vertex Points in 3D STL Model Creation