Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Digitized Pathology Data
2.2. Computed Tomography Imaging Data
2.3. Computing Cellular Density Map on Histopathology Slides
2.4. Pathomics Feature Extraction From Cellular Density Map
2.5. Radiomics Feature Extraction from CT Images
2.6. Experimental Design
2.6.1. Experiment 1: Identifying Pathomics Features to Differentiate ADC from SCC
2.6.2. Experiment 2: Identifying Radiomics Features to Differentiate ADC from SCC
2.6.3. Experiment 3: Exploratory Identification of Cross-Scale Pathomic-Radiomic Associations
2.7. Statistical Analysis
3. Results
3.1. Experiment 1: Identifying Pathomic Features to Differentiate ADC from SCC
3.2. Experiment 2: Identifying Radiomic Features to Differentiate ADC from SCC
3.3. Experiment 3: Exploratory Identification of Pathomic-Radiomic Associations for Differentiating ADC from SCC
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
Abbreviations
NSCLC | Non-small cell lung cancer |
ADC | Adenocarcinoma |
SCC | Squamous cell carcinoma |
HV | Horizonal-vertical directions |
D | Diagonal direction |
SVM | Support vector machine |
CT | Computer tomography |
FDR | False discovery rate |
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Parameter | CPTAC (117) | TCGA (54) | |
---|---|---|---|
Magnification | 20× | 117 | 29 |
40× | 0 | 25 | |
Resolution (μm∖px) | 0.252 | 0 | 25 |
0.494 | 117 | 0 | |
0.502 | 0 | 29 | |
Gender | Male | 82 | 23 |
Female | 35 | 31 | |
Age at diagnosis | 65.1 ± 9.3 | 68.6 ± 10.00 | |
Grade | Well differentiated | 8 | NA |
Moderately differentiated | 109 | NA | |
Poorly differentiated | 0 | NA | |
Undifferentiated | 0 | NA | |
Pathologic Stage | I | 58 | 19 |
II | 38 | 18 | |
III | 21 | 12 | |
IV | 0 | 3 | |
Discrepancy | 3 | 2 | |
NSCLC subtype | ADC | 61 | 25 |
SCC | 56 | 29 |
Parameter | NSCLC-R (101) | TCGA (45) | |
---|---|---|---|
In-plane Resolution (mm) | 0.97 | 0.6–0.97 | |
Slice Thickness (mm) | 3 | 2–5 | |
Field of view (px) | 512 × 512 | 357–512 × 357–512 | |
Scanner | CMS, Inc. | 11 | 0 |
Siemens | 90 | 10 | |
GE Medical Systems | 0 | 33 | |
Philips | 0 | 2 | |
Patient Position | Head First Supine (HFS) | NA | |
Gender | Male | 64 | 20 |
Female | 37 | 25 | |
Age at diagnosis | 68.5 ± 10.4 | 69.3 ± 9.8 | |
Pathologic Stage | I | 17 | 16 |
II | 13 | 15 | |
III | 71 | 9 | |
IV | 0 | 3 | |
Discrepancy | 0 | 2 | |
Subtype | ADC | 49 | 19 |
SCC | 52 | 26 |
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Alvarez-Jimenez, C.; Sandino, A.A.; Prasanna, P.; Gupta, A.; Viswanath, S.E.; Romero, E. Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results. Cancers 2020, 12, 3663. https://doi.org/10.3390/cancers12123663
Alvarez-Jimenez C, Sandino AA, Prasanna P, Gupta A, Viswanath SE, Romero E. Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results. Cancers. 2020; 12(12):3663. https://doi.org/10.3390/cancers12123663
Chicago/Turabian StyleAlvarez-Jimenez, Charlems, Alvaro A. Sandino, Prateek Prasanna, Amit Gupta, Satish E. Viswanath, and Eduardo Romero. 2020. "Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results" Cancers 12, no. 12: 3663. https://doi.org/10.3390/cancers12123663